{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "33c24707-2f57-4496-bf7e-f80069fa4061",
   "metadata": {},
   "source": [
    "# Classification Models\n",
    "\n",
    "Darts provides a comprehensive set of classification models for time series forecasting tasks. These models can predict categorical labels or class probabilities for future time steps based on historical data and optional covariates. The classification models in Darts include:\n",
    "\n",
    "- **SKLearnClassifierModel**: A wrapper around any scikit-learn-like classifier (default: Logistic Regression)\n",
    "- **XGBClassifierModel**: Wrapper around XGBoost's XGBClassifier\n",
    "- **LightGBMClassifierModel**: Wrapper around LightGBM's LGBMClassifier with native categorical feature support  \n",
    "- **CatBoostClassifierModel**: Wrapper around CatBoost's CatBoostClassifier with native categorical feature support\n",
    "\n",
    "This notebook demonstrates how to use Darts' classification models for time series labeling tasks, where we predict categorical labels for each time step based on recent behavior patterns. \n",
    "\n",
    "*Note: The labeling task shown here is purely for demonstration purposes to showcase the classification capabilities of Darts' forecasting models. Since we artificially generate the labels using the same patterns that we later use as features, this is not meant to demonstrate model performance or predictive accuracy.*\n",
    "\n",
    "We'll show how to:\n",
    "\n",
    "1. Generate synthetic time series data with categorical labels\n",
    "2. Create and train a classification model using features derived from the time series\n",
    "3. Apply the trained model to label new time series data\n",
    "4. Evaluate model performance using classification metrics\n",
    "5. Generate probabilistic forecasts with class probabilities\n",
    "\n",
    "Note: In the final section, we'll explain how these classification models can also be used for traditional forecasting tasks (predicting future categorical values) using the same workflow as regression models, but with classification instead of regression as the underlying task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "19b82114-ba19-442f-9c97-971e85843693",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix python path if working locally\n",
    "from utils import fix_pythonpath_if_working_locally\n",
    "\n",
    "fix_pythonpath_if_working_locally()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "db5ed99b-6f5d-4f3c-911e-c41adce7f558",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import darts.utils.timeseries_generation as tg\n",
    "from darts import TimeSeries, metrics\n",
    "from darts.models import CatBoostClassifierModel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c66bbae-060a-46fd-8205-c7f59564fd7c",
   "metadata": {},
   "source": [
    "We generate a synthetic time series with trend, seasonality and noise. The frequency is hourly with a 24 hour seasonality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "06b6bd13-3847-4f1e-a8e5-2f2fb040f2f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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czz33XGi7fv36MhvGJkxQHt5NmFSoUCEqYWIu7AhCdXO7QJjY6qgcsnQ4dnPlmMIE+7zSoG0WE6/KsMmAwoQQQkjGYQZ23nzzzVZhsmjRIldhAvdOTiwmOKcuThDnYbp2bAJi7dq1stmfX1eO2RUY+7wsJm4ZRKmEwoQQQkjGYcaLbNiwwRpjgtogbsIEMRfm89wCXPXzqXO2aNEidN+s2rp9+/awMvkKW9yJmzDJnTt32L7Jkye79vtBA0HTlXPJJZeIIEBhQgghJOOwuUFswmTp0qWuwgTWEjM7xs1iYmbw4Jz68StWrIh4DsSJyc8//2zd5yRMTOuKaZkxad68eYTFxIyHSRUUJoQQQjIOM5YDC7ce6KpAl2G9ZonNYtKyZUvfwuS6664Lu3/OOee4Hg927NjhS5js2LHDKkyGDRtmFV1uIBbGtJj06dNHBAFWfiWEEJJx2AJKbS4Tt/gQZTExhYVbSq3pyqldu7bYvXu36+seOHDAtzDJkydPxH5VUE0HPXhs9VH094CCbzrnnnuuyGiLCd4w6v03aNBAKkjVIhqqFequYcOGokmTJmLKlCmJGgIhhJAUghgHuC5sQafJFiZIHbYt9jpuJdqRvVO4cGExcOBAa78dndGjR8vslsaNG/uymNjGNX/+/Ih9mEeb1ceGV9fiCy+8MOJzCUINk4RaTB555BH5oYwdO1bmbffo0UNccMEFYtmyZWL58uXivffekz6w2267TVSsWFGqSkIIIZnDo48+Kp544omwWiKpcuUgGNat9LxTDQ+VkXPjjTeKtm3b+kqn7dmzp2jdurWML8HxXmXevQRTLHilCtvEVRDK0SdUmOzdu1c0bdpUvnmYh8qUKSObD82ePVumbRUoUED+wXf34YcfWoUJJtacXPjEEqHqVOCQGUBE7HC+kgvn2z+cq+DMI0QJWLhwobwQTWbxLnPtQOqsLT7DXJghJvSmfVhz9DmwZbrY5grrG47Fn9eapce5+AXWnWjjSnQwVtOqBBGVyP8bL0tTwoUJ1CX8XrfeeqvYuHGjTH2qVq2a+P7776WFRC9e46SkJ0yYIMaMGRO2r02bNlK1JgrVdpr4g/OVXDjf/uFcBWse8dtvi49IFGafGKxDthgSHaTyop/N7bffHtqHtcvsbxPtXHllyHjFvpx88skRQguxLLbXw3v0EmAqE6hIkSIR+xKJrTdPUoXJpZdeKs1448ePl/cffvhhOZFQePqXA9tO5rUuXbqIDh06JM1igg8ZXST9qrpshvOVXDjf/uFcBXMeixYtKteATz75RK4N+H2Hiz9RmHEdCCnQLfOwjpgFyLBwmtYLxIh4uW+85srWg8cp+PUMi7DIly+f+Omnn8L2nX322VZhgjFAhOnUqFFDrFy5MnQfBgMUoMO6/Mwzz4T227oNp4KECBN0a0TxFrzpRo0aSaXcq1cvaR2B+UmfdGw7mfcgQJIdjIMvFX/M/MP5Si6cb/9wrlI7j7Y6IjjP1VdfLe9/8803MhYjUZgZKRAPEydODEsNNi0VEAWmWPEKIvUzV14uLL3oWpEiReSa6ZW1A1eRDVvKc506dcKECdZijNMcV1D+XxIyil27dkm1iuBXfKhw3aBNNIJey5UrJ7Zs2RI6duvWrXIfIYSQzAEJDl7BmGaAajwxX8+MnShRokTEc7BQm5VZ44EtKwcBwX7SjYEt7sMmTCCs9BL7ClhWEOeDgmoQPnfeeWfosTvuuEPewoIVFBIiTGAOQhrSvHnzZOAP1N+qVaukSoNafv3112UUMiYLacSoQEcIISRzmDlzpqcw0a0TKCNxww03iG3btsXl9b0CQ+EKMYFFH24T3f0UD2zZLjbLBihcuHDEPl1IuD0fsZ02sN4ivAJ9eTC/eqzPSy+9JDZv3izGjRsngkJCXDmo2Q+/1ciRI2XaMJoLtW/fXlx88cWiVq1aUpC0atVKqrdOnToxVZgQQjIM0y2AgNJPP/00olZHs2bNxLp168T9998v96FOx5dffpnj1/eqnaILEN1ignonSLDAxfS0adNEPDD72DiVvwfVqlUTs2bNCtunJ4y4jd/JvYPwCidrDOJnYDQIEgkLfoVPC3+2L+t9990n/wghhGQmZmzG3LlzxahRo8L2de7cWQZ1QgQovvrqq7i8vpswgfvCFk+hxMJbb70l4olNROgdh/XkjpVaLIibsLFZTJD98/TTT4sHH3wwbD9KdKQTwYh0IYQQklHYFn4kQdhSevv16xf313cTJkiqMK0HZj+cRAsTNMwz05cRi1KjRg1fwgTWnaFDh4btQxFTW+yKOe9Bh8KEEEJI3PGT4aEyZxD7kExhgjACUwAksvgbwhnMlGOk9ZrCBHExXbt29SVMEA+j3F8KBLCaZfWvv/56a6n9IENhQgghJOkl0RONW/ArLCamK8Sr+FpORRrEiZkVZAoOCLUCljgRm8izZfogyNbsGJzMonbxgsKEEEJIjkFKq14h1asvDUDgq1ni3RZ7ES04p1s1VQgT04qQ6HL5pjunUKFCVkvIqYbgQNyLTWSp8Y8YMUIeM3z4cHm/atWqEe1h0g0KE0IIITm2jqBbLRbI6dOny316vxk3MWMuurbMkWhB7Iqq4GqrugphYgqRRBfz1IUJLCB58+aNsKLYUosXLVpkFXlKwCB+BJlMvXv3lvdNi4lZMC4doDAhhBCSIz7++GOxevVqWTANMQ1r1qwRK1as8HweLCxmLEisC+kXX3whhgwZIlNj9UqptuxQmzCJVzaQE7rgQrwJxIkts+YEI5sJqcI2N5Nu8XErma8KqKUTCUsXJoQQkh2YwavIDvEDqqya1V9jESYoIHb55ZfLbRT0RH0sBSwmsFboPXBglTCFSaItC8jCMYN+nYqsmVxzzTWievXqYWnVfgNa07GyOi0mhBBCckSsizpakphBsmazOj/AWqN49dVXwzoLI5jUdJnAAmHGfGDxTyQ2F5U5LtVHaPDgwVK0vPbaayH3DFq6xFLOP9n95uIBhQkhhJCUZOAgdmL79u1h+xCbYnbXjXbxNYWJmZkCYYLn3HTTTaF9ffv2FYnEbGpos3oooTJgwAA5D927dw89BteP6gSM41Ah1g/RNCEMChQmhBBCckRO3CA2EQLXTE7OsWHDBldhotw4kyZNkl2OMX5bhkyi5kiJBTMDR2/Wl8sSN9KnTx/xzjvvyDG7jRc1UuLd7yeZMMaEEEJIyoTJ6NGjI/bpacd+QCl2HZRl97KYqEJr6N+WbKuSyrwxhcnAgQNdzwErDxodejFnzhzx0EMPyQa5Tj15ggyFCSGEkJQVU0OHeZswQS2Sr7/+Wi7iF110UVTCRAeixAx0TWQxNSfOOeec0HbDhg2tQcNmDZJYOe+886zzmi5QmBBCCMkR8c5oQcrvwoULRf369eV9pB+7xVS4CRPEcZgxKIkupmbjrrvukmIBbioE6Krg32jL+GcDFCaEEEICJUxgMUEAqOL888+PqBCr88QTT0QlTFJhMcE4ILbwPlT8iEobJuFQnhFCCEmYK8dWq8OrfwuESTSLtlvZdcRxmNVUU2ExsQW1tm3bNmXjCDIUJoQQQhJmMTFFAfBKB4Yw8Zsy7NasL0gWExvoBkwioTAhhBCSMGFiq6Nh9nOxCZMGDRq4HqNcO1u2bHE9DhYT8/X8Vk1NNOlYYyQZUJgQQghJmCsHrgszcHX8+PGewgSpvE5F2S655BKZwYKsFjOzxQQixKwmyyDTYMNPhxBCiG9efPFF2RgPgZx+LCaVK1cWM2fODN1HKXiv8u8QJraOuuC5554TS5YsEevXrxe33367Z6oyXEk7duywFh8LAsjQKVu2rKdYyyYoTAghhPiO50D10cWLF4t69er5EiZjxowRZcqUEevWrRO9e/cWs2fP9gw+hTDRxYRqzgc2btwY2rdo0SJXYfLf//43okR9jRo1RJDo0aOHfG+MN/n/oTAhhBDiC6dS8U7iAFaA0qVLy+0qVaqI4cOHS2sLYj6cXDVKmCghYooMPV4EmTtuwqRw4cLy9tlnn/WVWkyCAYUJIYQQ34XPbB1udbeOV0ddhWk10V0sKJhmZuWo7B5dmOD13aw1hQoVkrfXXnutmD59upg/f76siUKCDYUJIYSQmITJ7t27XdN1bTVMnM7Vq1evUFDq77//HhFj4iRM/FhMcN6WLVuKyy67zPFYEhxY+ZUQQogvJk+eHHZ/3759rhYLN2FiguwapPZCkODPFDxKkOjC5M8//3QVJspiQtILChNCCCG+ePnll8Puw92i3Dk5FSZLly6Vqb0QJYcPH454XFlM9OJobsLEVvGVpAd05RBCCIkJxIIcPHgwLhaLvn37SjFhZtEoVPVWU2w4uZLcgmtJsKEwIYQQErMw0V05pmhwEwdmvMeVV14ZEiaIMTFRlhHTdTRw4MCwGimKX375JYp3QoIEhQkhhJCYhQncKX6ycEzMtF2IEiVMbKjX2bRpk+MxFSpUCG03adLE91hIsKAwIYQQ4ouKFSu6CpOCBQv6PpdpTUHmjJswURaTbdu2OR6DeiUXXHCBPM+dd97peywkWDD4lRBCiC/++eefCGGSJ08eqyvFC1sjPz8WE6cibwD9c1auXCldQUFp1Eeih8KEEEKIL8z4DtNiomfMxFuY4LW8hImqW0JRkt7QlUMIISQuwsSrB05OhMmuXbvka5kVYRXNmzf3/dok2FCYEEIIiUmYjBo1KqyOSDQWE1vGjpsw+fHHHyP65+iwZknmQGFCCCHEF7Yqr6j+aksBvuuuu1zPdcIJJ0Ts++677xyPR9zIww8/7Pi4m6gh6QVjTAghhHhy/PhxqzD56aefQtvFihUT8+bNE8uWLRPdunXzPJ/J2rVrXYXJjBkzHB+nxSRzoMWEEEKIJ3osiY5ekh7VWRs0aCDuu+8+kTdv3qjPV6BAAcfjIYo6duzo+DgtJpkDhQkhhBBPnHrSvPHGGxFl4/1QpEiR0Ha5cuXk7TPPPONqMVHdh21QmGQOFCaEEEI8cesiHEvwK6rETpgwQbRr107MmTNH7itVqpRjCjCyclTKsA03NxBJLyhMCCGEJF2YgM6dO4upU6eKSpUqWS0ujzzySCiGBb1vVq9e7XiuvXv3RvXaJLhQmBBCCIkAFVTRz2bNmjW+hUk0dUxsmMLETClev359VFk+JD1hVg4hhJCIjJlatWrJW7hbjh07JgYNGhR3i0m0wsQNCpPMgcKEEEJIGHv27AlL50Wn3q+++irhFhNTiEQjTNwCY0l6wU+SEEJIGGY/Gj+iJB61RMwy9dEIk2uvvTZHr02CA4UJIYRkOdu2bRNPP/202Lx5s7zvlv3iRq5cuXI0DvN1zW7GbvTt2zdHr02CA4UJIYRkOa1btxYDBgwQ7du39yVM8uXLl5BxnH/++WH3jxw5Yj2uVatWEftYxyRzoDAhhJAsBov/ihUr5DZKySO2xEuYIMU3EZgxKk7CxFbvhGQOFCaEEJLFmPEkv/76q6swGTx4sGjatGnCxqOLEydhYsaikMyCwoQQQrIYpALrHDx40FWYoHw8YknMLJguXbrEZTz9+/cPbbds2VLGvtiESf78+UP3P/vss7i8NgkGCZWdkyZNEm+//bas2FeiRAkxZswYmec+ceJEMXnyZBnYdN1114nevXvnOGiKEEJI9Pz2228RPWnchImqNWIGpo4YMSIu4+nXr58s5oYmgM2bN5epyg8++GCEMPnvf/8revXqJTp06CCuuOKKuLw2yXBhAkGyaNEiMW7cONmsacuWLTL1a8GCBWLatGlSnCBY6c477xSlS5eWypgQQkhqhQma9bkJE6cU3ty5c8dlPEg5fvLJJ10bA2IMLVq0kH8k80iIMPn777/F+PHjxdixY0XRokXlvooVK8rb2bNny4hqWFDAzTffLD744AOrMME/iNnREko5mg6WflHqP5r0tGyG85VcON/+4VxFB2JKdGCtgJXbrSeNObcjR45M6nyjymuQPl9+5+JbBC8hwgRNl2AOhN8PLbGhpG+55RYpSJAvrwdOVahQQWzdutV6HpRChvtHp02bNqJt27YiUezcuTNh585EOF/JhfPtH86VP7Zv3x4xb24N8c4777yI5yCA1tyXSCCckvl6fuF3zp2yZcuKlAoTmAJ37NghZs6cKT+s22+/XZQpU0aaDfV+Ctg2g6/0YCr4D5NlMcE4S5YsydLGPuB8JRfOt384VzlL0S1QoIBrzF+dOnUi9uE5cMkni8KFCyf19bzgdy6+JESYqLLE3bt3l3EkcOMggGnhwoXyn0A3HWL7tNNOs54HAiQRIsQNfKn4xfIP5yu5cL79w7nyh3lhCFe8/htdvXp1sWrVqtB9pzlN5lwH9bMN6rjSjYTMIJQsgpN01a22YcpBIKwCbpzy5csnYhiEEEI8MANdzeBXJDJ4oTf8SwYQTyRzSYgwgQUE6VvIyMGXHHEln376qahbt65o1qyZeO+998SuXbtkvvyUKVPkPkIIIakPfv3zzz/DhAkuJlHpFVbvL7/8MhDCJJrmfiT9SFi68AMPPCAef/xx0bhxY9lXoWfPnqJGjRqhvgydOnWSfjlk46CWCSGEkNQLE5R5UCnEcMXDNdGuXTv550Sihcn7778ftk4k28VPMkSY5MmTRwwdOtQxqDVeVQIJIYTET5ig0qoq76DiBb1IdJrstddeK5Mnfvjhh1D1WZK5MEqHEELShGHDhskSC++++27czmkrpoZyD9FYJpLhypk+fbp87ygZAbcSyVzYCYkQQtKE+++/P+QOj5cYMC0mqsial8Wkffv2sk4VqFevnkg0yA7avHlzwl+HpB4KE0IISTGofr1nzx5ZiFKvKwIXiUo/TZS7xCZM1D43YTJ8+HDZz6Zy5cqiVq1aCRkbyU4oTAghJIUg3g69w8Du3btl0gDo1q2bzGxUrpVECRObK0cJEzdXzllnnSVGjRqVkDGR7IYxJoQQkkKUKAFIy1UoUaJ60ai4DxO4dNatWyf++uuvmF7/yJEjjo/5DX4lJJ5QmBBCSEBAJ3Ywb968sP0bN26MECbKgvLwww+LqlWr+q4HhQKXS5YsCcWooJ6UE0zLJamAwoQQQlKInvqqxMaVV14ZdgysIaYwUbVGBg8eLG9RxNLLaoLmfFWqVBGXXHKJ7PQODh065Hg8LSYkFVCYEEJIFKXQlSDwYt++faJ58+ayZ5hbfIguJlDcbO3atRECA69rChPEhmzYsCGi664bQ4YMkZVdVWYPzun2fihMSCqgMCGEZD0QDnr/lQMHDoiaNWvKTrpqsUd7jQsuuEAUKlRICggv7rjjDmmVGDt2bCit1gbOq4Nq2Ki4qgMxYRMmqO2hc/ToUdcx6a+F9+tmLQF05ZBUQGFCCMlqIELQSBR/P/74o9z34IMPihUrVojFixeLQYMGhcqiI8gUFoamTZt6nhc9wRTLli3zLUzQ2NQsIIYsGZswMS0xXsJEt8SccMIJrvElgBYTkgooTAghWc29994rS51v375dChKwfPny0ONLly4NKzrmx2ViorqrI+D0u+++CxMI+nmduufi+aYwwRjM4/bv3+86Dv34E0880VPI0GJCUgGFCSEkq3n99ddD26oXi74gK+GgCp0p3GIzYG2xgUDVc889N8wiYlpMgIoD0Tu2m8Lk559/jrCYIOPGCYgiPQUZwkQ/Z+HChSOeQ4sJSQUUJoSQrMW0VpxxxhnyVo/xUMeY1gUEkjqhLC+KY8eOhVJ7wRdffCGFDcSCKUJsYgXWEVOYoNuuaTGBW8qJzz//POw+XDlqXKBUqVIRz6HFhKQCChNCSNYCq4NO7ty5IywFTsLktddeczyvKRheffXViGMQI2KzlgBTrMyZM8daOh4pwjpuLiZkCemg9L0udooWLRrxHFpMSCqgMCGEZC0q9sMUJrrbRhUi27VrV9ixbjVD4HrxEgYQEU7CZOHCha7BtIpvvvnGs7y8wnRFlSlTJkyYINvIRFmQCEkmFCaEkKzFtEwoYaLHbqgFHWXh3Z7r9RiCbHVuvfVWR2FiY8eOHZ7HuFlM4LrRyZcvX5grh8KEBAUKE0JI1mILMtWtJMAsYqZQomLTpk3iyy+/DHuOTXAg3Vhn/vz5UQkTPVPIiWgsJmZtlAIFCkQ8Rwk1QpIJhQkhJGsxhcnq1avlrS4ygK2BHkQF6p6gT02DBg3ElClTXFOAzXMCv1Vk/fLuu+/6dluhuJr+vuDaMaEwIamAwoQQkrWYcSIIMrWJCJuLBM8dPXp0SNzccsst4vvvv5fbNkuITYSogm7xZOfOndb9plhCITc9oDZPnjwRrpuzzz477uMjxAsKE0JI1uIUJ+JHmFx77bURcRsPPPCAvPXrokmEMIFbyY8wQdVXJcRUijRqrOiULVs27uMjxAsKE0JI1uJXmJhpxaBYsWIR6bRr1qyJSpgcOXJExBunbCGbO0rP6oEwMeNQGPxKUgGFCSEka7EJEyzgpjBRsSemADjppJPC9hUvXjwqYeJVEv7MM8+M2NepUyfX59gEiFdgrAr81YVJ7dq1XY8nJFFQmBBCshabdWHv3r0RwgSpvSZo6IciZTqo6BqNMLFZYnTq1asXsQ9BqjfddFPUwsRLBMFictddd4V1RyYkFZyYklclhJCAWkzQZM+s3Gpj0aJFol27dtbHbFk5sdQmsWXFQECYlhodvTZJtMKkffv2shEghFmHDh0cA2kJSSQUJoSQrMVm2XArFe833devMFFZPNEIE7hc3IRJrBYTZOXAldOnTx9532wQSEiyoCuHEJK1/PTTT1ZR4VeY2CwesDbEIkzy5s0bF4uJkzBxGxPETsGCBX2MmJDEQ2FCCMlazP430QqTLVu2ROxDbRC/wkR/fVt3X1sTPS+LiZMrR3dbmb18KlWqFFGAjZBUQWFCCMlaDh8+HLEPosRNWNSoUSO0vW3bNteA1vr164tWrVr5GgvSj/1Ui43VYuImTFC9lpCgQGFCCMlabCm0sHjs3r3b8TnXXHONa4yIXk0VFg+/1VNtrpyzzjorbjEmbsIEYoeQoEBhQgjJWmzC5P7773dc3E0BYQsQ1avEnnzyyeLNN9/0HAcqyJqpx8o607Fjxwgrih9XzqeffiqeeuqpkFXITZhgnIQEBQoTQkjW4lV0zIZNQDi5cpDpgmZ5fs5pExuwZEyaNCms8BnEkCkkrr766tA2RBXKzTdp0kQ89NBD4t5775X7KUxIukBhQgjJWqIVJs8884znIg5RoFdutQWw2oTJiSdGVm/Inz9/qP+N6mUDEWJm67z44othwkSVxgcTJkyQtxQmJF2gMCGEZC3RChO4VbwWcb3/DKwgZvwGOhKbQCjYhEm+fPnkbd26dcWBAwfEt99+K4WOGY9SunTpMGFiixlRwgTZN6ZYojAhQYLChBAisl2YmBYEJwoVKuS5iL/11luhbYgA8/iePXtGPAdCwyZMChQoENZQTx1jVqzFayh3D2JMbIXf1HMglvbs2RPxfEKCAoUJIURkuzCxFTIzWbFihRQGXou4LhpatGgRlop78803W58DAaKLEK/uvsrFo9cgUeIKFhM9M0gF9KqgXAiTrVu3hj1OYUKCBIUJISRr0YXJyy+/7HrsBRdc4GsRP3LkSGj70ksvFa+99pqsUVKlShXxyiuvyP39+vWLEBqVK1cO29erVy/H12jZsqWskQIXzqxZs+Q+5b6xCZNhw4aFisHZgmxZfp4ECfbKIYRkLcqKAGGix2nYUK4SvxYTBLQiKwcBsNu3b5cpwaq6auHChSNcOabVxi0lGGNAQCxSh9U5lcUErhxTmJjnvf7668V7770X2kdhQoIELSaEkKwEC7iqVwKLhVcacLRuj6JFi4ZEA1xAesn3IkWKhB0LUWIGrLoJE4V+TjeLiQ66B8OCouMnc4iQZEFhQgjJSvbu3RvaRnyHUzwH6Nq1a9TCxK0pnvkYRJEZgGsLhnVDFyZOXY+dKsrCskNIUKArhxCSlajYDCU2bMIEdUvgwunSpUvU5dvdMn3Mc+C1zeP9WExsr+flygGm2+imm26K6rUISSS0mBBCshLdDQJXhk2YIMC0b9++YRYGW08bvX+Ofk4nzMfiIUyU2EG8iK05oaJ79+7y9u+//xbvvvuuTB22vSdCUgWFCSEkK0EXYV1Y2GJMbNYRVfRM57zzzvP1XDdhEkuMidM5EUfixAsvvCBvYQlCEKzfJoOEJAsKE0JIVqLXG3Fy5djcMciy8SNC3ISJGadis5jAohEN+jl37NhhPQZxK26xNIQEAQoTQkhag0DPaBdx02IC64RNhNjEBdJ+/RyXU1fOggULRKzCZOnSpdZj/MbHEJJKKEwIIWnL8uXLZVouipep1N9YLCYQJnoHX68A1iuvvDKuFhO4kczjo60toosdJdRq1arl+rqEBBEKE0JI2oIKqCiStmnTJllhVV/Ux40bJ9544w1ZhEyxevVqccstt4jZs2eLqVOnei7YTsLEDBa1Hae/rh+LiSmM3J5vw/YeypYtK5o0aRK6f+jQoajOSUgqoDAhhKQtu3btCm0fPHgwtD19+nTRo0cP0aFDBzF//vzQ/ssvv1xMnjxZNG/ePKxfjAo01dOC3aweprDAcc2aNQvbt2bNmqhiTExq167t+Hw/YwJ//PGH2LZtW1TnISSjhQn+MWFKHDt2bGjfxIkTRePGjeUPxPDhw6O+KiCEEBu6xWHo0KGh7VGjRnlaDJQwgZVFWRjQG8fJkmITJkgt9lsgzSxoVqZMmYhjkKYcDbax4nUeeOCBqM5DSMYKE5hSn3/+een71YO5pk2bJsXJ22+/Lb7++mvx/vvvJ2oIhJAs4rPPPgtt65VPlTXCLQZFxZugtgl+kz744AN5Pr3WiZcwMYWImzCBiOjWrZvcbtq0aVi3YIWfjsfmOU3gYmJVV5JuJKzyKxpEod236t4J4Ndt1aqVKFGiRKgFOH4A4Cd2iprXI+flgH20HY8FFWjGZlb+4HwlF863N7jwwfzgzxQm2IdGek7gd0rNLX5flFvGab7N3yAIFTNbB8e4fV7oZtyzZ0/5O6mOe/PNN8XTTz8t7rnnnqg/a5sAwRj0IF+392TC75x/OFf+sAWYJ02YoOogAstgGdGbRcHXiasDRYUKFcL8vCYTJkwQY8aMCdvXpk0b0bZtW5Eodu7cmbBzZyKcr+TC+XZHiQ99gUCGCvZ/8803js9D0TQ34WKL3dBBfMvRo0fD9v3111+e50SPHlRe1eNKEB+jvxe/2Hrz3HjjjRFpx9Gel985/3Cu3EEwdsqECXy66L1gKnhcxehBXthGXwcnEIiG4LVkWUzwpSpZsqRvVZfNcL6SC+fbH6VLl464ai1WrJjcP378eMfnXXTRRVG9TuHChcPuwxVTqFChCFcMXjdZVKtWLWJf3bp1pegaMGCAvI+MJL9j4nfOP5yr+BJ3YbJx40axfv16a8AVcvX15lLYdmt0BQGS7Lx7fKn4xfIP5yu5cL7dsc0N3CzYrywRfp/nhpmtgyB+s4S8U22URGFm9iCgFq8PwYJUavwuP/zww1GPid85/3CuAipMVqxYIU2FykcL3y18r7t375ZmnC1btogGDRrIx+DGKV++fLyHQAghIQ4cOCBvndJmY7n4MYNfr7jiCvHggw9GdCZOJuZFnj5G1biPkKwUJmgKpRf0QYwJTKmdO3eWxY0Q2IU4E/wTTZkyRfpACSEkFmC58Kr4itRhiAQ9IFanevXqORYmuI9CbzrnnHOOCIowISSrhQl+KHQzJ/45VMpavXr1ROvWrUWnTp2kTw7ZONddd128h0AIyQLgPtFTclU1VlvfHD2W7ayzzgpZUQCC9KPFKTU3lZivz744JF1JWLqwYtCgQREBrWZ1RUII8cPMmTNlnZF+/frJmDW9HIGyiBw5ciTiebq15JJLLpHF05CuC2tK5cqVox6Hrb4JxjRy5EiRKjAfOrSYkHSFUTqEkLQAKbqwsCK75uqrrxZ79+4Nexz1OpCia7pUwP79+8OCRJ988klZBVYVOYuHMEF9JnXRFW3V1niA7BsdChOSriTcYkIIIfFAFxwIZDWLLyqXjW0/uhCbtRScqrr6weYuAhBNqHhtioRkZoSodGkKE5Ku0GJCCEkLTBeNWeRMuWxs+1euXOla7yNaWrRoEYozmTNnTthjqRAlesE2BdNWSbpCiwkhJPDARYNK0To2ywjEi23/Cy+8EBEkmxOQafjdd99JK048hE68wBypwN6FCxemejiExAQlNSEk8MybNy9in02ALF261Gox0YmHMFEFzIIkSsBVV10V2v75559TOhZCYoXChBASeGwxHTYBsmvXLk9hors7Mo1evXqFOhWPGDEi1cMhJCboyiGEBA40AkV9kTp16oiLL77YWiNkyZIlEfv27dtntaTE0kgsHYHogtUIVbUbN26c6uEQEhMUJoSQwHHPPfeISZMmyW3EcdjaySP7xQRdft0sJmiylupCaIkGbT7Y6oOkM3TlEEIChxIlqjGol3tGbwzqdmwmW0sIyRQoTAghgQZNQL3cMwpUgnU71uzASwgJHhQmhJBAg3ocfi0mECZux5500klxHBkhJBFQmBBCAp+RQ2FCSPZAYUIICTQQGtEIE7MSqw6FCSHBh8KEEBJoEDOidwd24+jRo2LWrFmOj1OYEBJ8KEwIIYEG1hJk27hRpUqViC7CNihMCAk+FCaEkKSAPjZdu3YVDz30kDh+/HhUFhO4aJxo1qyZyJMnj/WxGjVqhN2nMCEk+LDAGiEkKdx///1iwoQJcvuyyy4TTZs2tR5nihYviwkKpuXOndtX+XnVEZgQElxoMSGEJIWxY8eGtt063/75558RFhNUf3UCvWFQ0dWGWQEVpe4JIcGGwoQQknTcGumZXXEhTLZt2+Z4fMGCBR0rurZs2TLs/ocffhj1WAkhyYXChBCScEyxceqppzoeO2zYsAhXDroGA1ssCYSJ7Xx9+vSJ2F+tWrWox04ISS4UJoSQhLNz586w+24xI8uWLYuwmPz+++9y+/TTT7daX2xl6HHsKaecErZv5MiRUY+dEJJcKEwIIQlHCQvF5s2bHY8tV65c2P1evXqFCqyZQkNZTFAd1o8wocWEkOBDYUIISTimRcNNmJgiBvz444/yFkKjcuXKEcLEBoRJoUKFQvcrVaoU9bgJIcmHwoQQkiMgJFBx1Q2zpLxbiXm3xyBMTOECYfLPP/9Y04hLlSolnn32WdG2bVsxbdo01zESQoIB65gQQmLm4MGD0vXy119/iaVLl4YqsHqJDVtMiNOxpjBBt2EzxsRJmIB+/fp5vg9CSHCgxYQQEjOwSMBagl42PXr0iMlismPHjrDKrm7CBLEkej0UN2FiC5QlhAQfChNCSMzozfU2bNgQtTD55JNPZA0SWF1U8TNbjIli1apVolatWuLJJ58MpQSjmiuFCSGZA4UJISQuHDp0yPEx03Wj7t9xxx1SVKD53gsvvOBpMVGg3w6sJ88//3yoxL0JhQkh6QmFCSEkJvbu3Rt2/4QTTojaYoLGfqbFxY8wAXqsyVVXXRXxOIUJIekJhQkhJCZmzpwZdv/iiy8ObcOaMX/+fBkcC8ysHdX75owzzojokePmynEiV65cYa8PKEwISU8oTAghMXHWWWeF3d+6dWtoe8iQIaJhw4YyHgQixSxJD2GC6q8QFAoVJ+JmMTEzctw6B6usHEJIekFhQgiJCdN1o2fWDBgwQN6i+d7GjRutXX1xjC40jh8/7ilMHnvsMcfHTjwxvPpB3rx5fb0PQkiwoDAhhMSE6XJBhg6sHmaGDASMHkuiGDFihNVismfPHuvr1axZU9x3332O4+nZs6fvDsaEkODCAmuEkLgIE1g8jh07JtasWRO2H+ID+23oFhMIE9VF2Mla4uaeadOmjXz+4sWLXS0rhJBgQ2FCCIkJW5Aq4kZUEKsC9/V6Jzq6xQTC5s0333Qt5uYGznXvvfdKgbN9+3Yf74AQEkToyiGE+OLzzz8XVatWFYMHD3YVJrrYUDVLnCwm+/btCxMm+fPnd3x9dgYmJDugMCGE+KJx48Zi3bp1YuDAgVKA2OJGsN8UIW7CRD8HLB1mpo/i5ptvzvH4CSHpAYUJISRqIDZUjRJTmKBsvJMw0TN56tSpE3acGTR7zz33iAULFoinn35avPjii3F+B4SQoMIYE0JI1MBdc+DAAaswmTFjRoQwUTEmefLkkVYSuG309GKVJowuxYrixYuLunXryj9CSPZAiwkhJGpQNA0ixARl5c00Xd1igmqsqhCaqv6qQMyKLkzMuiSEkOyAwoQQEpMw0UWE4v3335ePmYJDCROk+55yyimOZeopTAghFCaEkKiB+DDTglVKrxkUC8GhCxMniwmEin5OChNCshMKE0JI3IQJrCNm+Xn0ybEJE/P5tJgQQgCFCSEkbq4cCBDTYvLTTz+FtvUYExMEwyIeRUFhQkh2QmFCCImrxUQvmgb0+3qMiQ3d2kJhQkh2QmFCCIkaiA2bMNm6dWtE8KseS6K7cmzA7aM46aST4jZeQkj6QGFCCImaO++8M+TK0YXGd999F3FsNMJk0aJFoW1aTAjJThIiTOAnRnfP5s2biwYNGojOnTuHdRydOHGiLG99+eWXi+HDh8tiS4SQ9GHlypUhiwlcM25iQxcmbjEmYOHChaFtM4iWEJIdJESYwJRbrFgxMW7cOPHFF1+Im266SfTp00dWf0SJ6WnTpklx8vbbb4uvv/5a1j4ghAQX0z0DlDCBZUMPWjVZunRpaBuixC3GRKdSpUoxjZUQkt4kRJjAXNu9e3dRtGhR8Z///Ec0bdpU+ovRinz27NmiVatWokSJErJhF5pzYR8hJLiokvI6ypXjFQuii5bJkye7Wkz0Xjo1a9aMcbSEkHQmKU7cHTt2yOJJJUuWFNu2bZNCRVGhQgUZMOf0g2ZeieHqzO2HLVZUAzGzkRixw/nKrvk2i6HpFpNoglTxOwB3jh/LDP7XY3m/qZ6rTIHz6B/OlT9gqAiEMEH64MMPPyzjTHLnzi2vvM4444zQ49h2aok+YcIEMWbMmLB9bdq0EW3btk3YeHfu3Jmwc2cinK/smG9cXJiokvK6lcOLXr16Wa0vNnbt2iWbBcYKv5vxgfPoH86VO2XLlhUpFyYw9fbv319aSuDaAbha0pt/YRuuHxtdunQRHTp0SJrFBF8qjNWvqstmOF/ZNd9mXxvdinLOOedYhYuNbt26iU8//dTzOFhhypQpk5ZzlSlwHv3DuYovJybyg4KlBFc8gwYNCl35QDFt2bJFZusAuHHKly9vPQcESCJEiBv4UvGLlbr5QkD0J598Ih544AFRsWLFuJ03U0jV99PmylEg0N0vuDDx4/rB/31O3yf/l+MD59E/nKuAC5OnnnpKHDx4UIwcOTKsHkGzZs3E008/LeNMYCmZMmWKuPHGGxM1DJJGbN68OfRdgLn/jTfeSPWQyL8cOnTI8TEni6cNZOT4qU/C4mqEZC8JkXZ79+4VM2bMEOvWrZP1SurXry//UPugXr16onXr1qJTp07y9pJLLhHXXXddIoZB0ozVq1eHtqdOnZrSsRD/wgRio3fv3mH7nOJOYAnxI0xYw4SQ7CUhFpOzzz5bLFu2zPFxxI7gjxAdsykcFsMCBQqkbDwkvMGeE5999pmsVTJixIjQvssuu0zWMIrVYkIIyV7oDCOBwczOuu+++1I2FhKOrS+OAoGvcOegThGEZP78+cVrr71mPdYmTBBXVK1atbiPmRCSnvDShQQGM430q6++StlYiLs1S0fVGipVqpTMTIDwsLlysO/UU0+NECYQNW61TQgh2QWFCQmsMPFb74KkVpigkrPCTWDgOGTn+REmTzzxRI7GSwhJX+jKIYHBFCJ6vRsSXGHy0EMP+TqHKgtgEyZVqlQJ26dXhyaEZBcUJiSwwoRdp9MjxiRfvnzW/Xnz5g27/8cff1iFSZ48eSIa9qGPFiEkO6EwIYENfmWhovSwmDhl2cyaNSvsPsoH2I6HgDGFSKxVXwkh6Q9/+UlgLSY56ZNCkidMnIqh1a5dO+w+Al+dhEnBggXD9vGzJyR7oTAhgYHBr5llMTEFi5MwgSvHFCaEkOyFwoQEBlOIqDRUEuwYEydhYlo9VAqx3l1cueyc4lQIIdkHhQkJrDBhhdDgsGTJEsfH/H5Of//9t7ytWrVqaN+FF14obwsXLhzahw6thJDshcKEBFaYNGrUKGVjIeGg5HxOG+6h47iymKAXEuqajBkzRu4788wzxYQJE0TLli0jgmYJIdkFL0lJwtm4caN49913Rdu2bUXFihV9CxO1kJFg4zd7SllMQLt27eSfTufOneUfISS7ocWEJBTUrqhVq5YYOHCg7CjthilM3AIuSXrQokWL0Ha5cuVSOhZCSHpAiwlJKPv27Qt1pl20aJHrsb/88ovjFTZJT0aPHi3Wrl0rM3IeeeSRVA+HEJIG0GJCUpbNcfjwYbF161a53bFjR7Fnz56wx2kxCQaowKsybGrWrBnVc0uUKCE/4/Xr10dUgiWEEBu0mJCE4pTyC+sIqnseOXLE8bm0mARHXKr2ALF0AWYFX0JINPAXg6TEYoIMDDdRAihMgsHvv/8e2oZLRjXjI4SQREBhQpJqMTl06JC8PXjwoOdzV6xYkbBxkdiFyaBBg1I6HkJIZkNhQpIqTNA/BW6Bxx9/3Nfz9+/fn6CRkViFSfHixUP39W1CCIkHFCYkqa4cBEIePXo0qnRjwPL0wREm9erVE40bNxbnnnuumDt3bkrHRgjJPChMSMyogEg3bIJi+/btvl8DmTmffPKJbPLWpEkTX69JcgbmGJVeDxw4YBUmqPT66aefig0bNohKlSqlcKSEkEyEwoTExI4dO+RV82WXXRa2cPkJfvUTX6I/v1mzZrIWChbDL7/8Uu6HKwiv/+2338b4DogTkyZNki63KlWqiGPHjoV9vqeddlpKx0YIyXwoTEhM3HLLLWLbtm1i4cKFYvLkySHrRuvWraWpX9UksVlMzEJqbuD5enYOrC0//fSTePTRR8Xnn38uF1ASX7p06RKK75k5c2aExYQQQhIJhYnlCp09WsLN+riCHjVqVJhAWLBgQWj7xx9/lLcTJ06UPXEgVnr27BkWI6KjKsH6wbS4oJfKzz//HLqvL5p4raFDh4r//ve/vs9P3MH8wmqioDAhhCQaChONsWPHyh9eXIWz6uj/AasExMCdd94p29vjCtoE8R9gzZo1oX0ffPCBvNUXtXgIEwgl03X00UcfyduXXnpJ9OvXT/bkUS4fkjPwf6DHBJ1yyikpHQ8hJPOhMPkXFPt66qmn5Pby5ctlCW0ipKVE57rrrouwKO3evVveQrh4NeaLVpjYXEGmFUY1B4R7R0GrSXyAMNy0aVPoPhvxEUISDYXJvyCwUsctoDObQAaGl9gYPHiw47E2i0k0MSa24Fnz+Yg5Ma/mbS4kEj0jR44M+wwrVqyY0vEQQjIfCpN/MdNQ3ZrPZROqeZvOxo0brcfGS5hcffXVrs9XaaxmkS89/gEBuUwtzjmoOaOLdLpyCCGJhsLEwUKSrsIEAarxXJDz5MkTsW/8+PHW133ttdfC9iEoNlpXDmJDdHfB6NGjI44xuxDDlQSxYwZmIhCXRM+ZZ54Z2q5WrVqY9YnBr4SQRENh8i/mApqOwmTVqlWiWLFiMl03XsG7+fLl89VcD/Nllo9HTxU/FpOSJUvK2xtuuEHUr18/rBvt3r17I57/2GOPRexDTIm5aM6YMUMkms2bN4vFixdnlHVGn8czzjgjTJjQYkIISTQUJhkkTK644goZb/H111+LqVOnxuWchw8fjjlIFUXYbBaT1atXh7aR3gsryZgxY0IWl969eztaR4BNdEHsmIumahiYKHbt2iWLkNWpU0fMmTNHZAp6cDP+D+jKIYQkEwqTDBIm+kJsW9BjQdUocXodXZjUqlUrbB8W65UrV0Ycu27dujCLSJkyZUS3bt1EgQIF5D7cd3sfv/76a8S+p59+OsJiEk32TywgPVmJpDZt2ohMFCZ4f3TlEEKSCYVJhggT070SL9eCynjxKimP+bLNGXquuHHBBRdE7EPasS2Q1itI01w0E+1eGTduXGjbZhnKFIsJXTmEkGRCYfIvw4cPT2thYqbHxmtRti24W7ZssVpMYolr0TNwdGLpyWIumokWJmZ2UKZaTNR3ABlaJ598cgpHRgjJBihM/sUM0ky3yq+mMPGyVPjFVs9l586dVmESi5jLmzdv3ISJWeAt3T7DZIA5scUDuVlMlEsMGVq29HFCCIknFCYOpLvFZPr06XE5r99Cc06uHC8Lh9MV+Omnny6iZd++fWH3bRlB2QxKy5977rkyc+u7774Lfb4IPJ4/f77VLQgho7KocufOnYJRE0KyDQoTB5O/11Vl0EDjPNuVL9wN99xzj7X2SDyFiZfFBEXQGjRoELbPrTqrX4tJx44dHa1EiYz7MN1ZEFhBTxm+9957xdatW2WM0JQpU0IBvD169BANGzaUWVRA/xxNiwkhhCQaChMHEYLeOekEGu2ZoAvvfffdJ+Nnbr31VrF27dqUCRNk2lSvXt336/oVJlWrVnV8LJHCRDUp1N9/0L8zepPFRYsWydu+ffuG9k2bNk2KWd0FpgsTWkwIIcmAwsRBmNiyUYKMLTUWwkRvZoeU2lQJk9KlS0cVOGkKk7vuusuaEaJXKU2mMDEbGQb9O4PPR7fynHDCCdbP2rRioeaMcu0wI4cQkgwoTBxcCmYV03TEFApvvPGGY58bG7hydgsg1YWGV4zJOeecE5UwMWNM4Eaw1dBwu4q31TuJFzbBpu8z411SDawhOnp1XT0mx02IRpvCTQghsUBhkiEWE1XW3UtwoVusX7w69Oriwcti0rJly6iuuE2LCYSJ7fm2BTYnAbl+sQXWqu8R3GdFixYVvXr1EkHBbAPgJExgZfOb9UQIIYmAwiRDLCbXXnutrzobtt43sbpxdPGAhU8t1nXr1o04Fs3gcuLKsVlMkLpqc6kkw2pimxslTJ5//vlQYGlQMIWIuq+7dPCeKEwIIamGwsRBmLj9QAcRm2XAtni6xWS4Pd8Wk4AGb7bS8YULFw477plnnom6OJcfiwnO51WrRE+DjSfDhg1Lq0wu8/NT9Uh0sYc4Jbf+QhQmhJBkQGHiYZZPZ3FlC4i1CQw/wqRgwYK+hclZZ50VdlypUqXkbTSuHDPGJH/+/BGCB8XZKlSo4HqeZLrk8J3xGyycbMzCaLCYIL1ZtyihrD+FCSEk1VCYWPzvmSxM1q9fL28XL14sLr300ogrf7h/HnjgAfHuu+96ChNdPOhdiHXBos9lkSJFfL8fM6gV6cZwB5luKZvbyKuvTyKtVmbK8BdffCGCgOnywn18B8zvC105hJBUQ2Hy75ViugsTm9XHJkwmTJggb+vUqSNrWdx///1houLuu+8WQ4YMEa1bt5aVQv0KE30O4YZBeq9CFVYzuw+rWAwbpsupfPnyEWJFlbO/6KKLwvbfeeedoW03C0C8wXfms88+C9v3+OOPiyB+PyBA9c9XfV/csolUtVhCCEkkFCYZIkxsVh+bMLGhH4eUYsWKFSt8CxP99RG38NRTT8m/999/X1o7bBaT3r17+xYmsMKY7h0lVMzYlfbt2yf0czQ7OeuvdfPNN4ft83LtjB07VgwaNCihqc22caDfkS5I1ffgq6++cjxHLAX6CCEkWmibdRAm6dYrZ9myZb6FiWnWVwGk5n6UL1eUKFEi4jyFChUKbaty5kqYIFj1wQcfDDseMSYQFyh8BpHiFu9i65Vj7lOBm6brSI9liVcjP1hhUPZ/4sSJoly5ctZjbCLITRh9+umnonv37nIb83H77beLZFlM4HIyXw/7fvzxx1CcUKZ2TyaEBJuUWEzgx4bLoF69euL6668X33zzjQiaMMGChoUaAYLoK/Lmm28GthcKFh3be3C6Cp81a5b1arpdu3Zh+5cvXx7aRixHhw4dwh7XS8x/++23oW1bITTFRx99JG677TbxySefuLyjSLFhEyZKgKApnVMhsHgITFQ/HTVqlLxt3LixY0VZW5xPpUqVHM+LVgGJcvngs//8889DwsgUJk5do5UYgRAtUKBAXMdECCGBFSbPPvusdA3AHw+BgivrVPYZsS3q6sccTeJgnr/pppsCE8hoW1BsOFlM9u7dG7FIISjWrA6qx2cgnmPy5Mlhj+tWFF20uQmT+vXri1deeUWcf/75wo3LLrsstN2zZ09rCrESJmbGiR6kGQ+LiV7TBsG0TlYQzDe69zrVD4ErZM6cOSHLlC4cowkM9kOrVq2kiIIItAkTLxcX5vDss8+O65gIISSQrhxcbc6bN0/GHmABQ2AkAhtRb8IsEoYfT/MHFD+Y0dTD8IMuirAAq/voyKsvxoiZQBfWdAh8BU4ZFqblAf10Lr/8cldhgwXWdPXY4k4APh+vwmdeYKGeOXOmdJ9AvOJ8putHvQ6+N3Cx6GNV4PuT07GYgki3jCCNWc0zYjbM18Jj2IegUgTp4rmTJk2SYlc/1ja/OuoxP+8FIhFuIoB5GTdunON3xAn8n11wwQVi3bp11nEEmWjmijjDefQP58ofbpW6UypMEIuAhVG/QkQtiu+//96aQTJmzJiwfW3atBFt27aN65h0CwJSUJUwMYMzMalmJkMQ+OGHH8JcIOpKXMULmJiuiBdeeEGKQzdhAusJ3juqmfbv319cc801EfVKFPh84zFP6ByMP1h0cD4zWBNuGuw3hapeuwSfJdxHsHzdcsstrgG3upUFz1PCa9euXWGP6+8NQlYJE1iuTBcJxoLj4bZRgqZTp07ScqSLBfVeYrWOuX2+OG+08SII8IVVSw+GVudKF/zMFfGG8+gfzpU7ZcuWFYEUJvgxNuMHcN/myunSpUtEXEMiLCZwSSBzBAsfes44/fii/wm65CabL7/8UmbIdO3a1Vq5VV848bgSJkuWLLGezyYCbXOqZ58UL15cvncETPbo0UO6T3RBpHPxxRdbe/fkFMy/DoQDxmRa1fTPCO+refPmcvvFF1+U3ymIHTdrA1Kply5dGrJsbNq0yTHoF+4ONQ8QF2YcEvZhPGZxOezDayhgPXT7buFKDD96mFfzqgOCRz+/KaRwXtuVim4dNEHGE9xBEKGKt956KyXf/2hxmyviH86jfzhX8SXpwgRmcTMoE/dtWRhYVOItQmxgwUJNDQgSlE9fsGCB9bjXX39duj3iARYEuCbcuuOq+IZGjRrJ7c2bN4vRo0dHHKMvzMiGMWNIQI0aNcTKlSvlNuqU6Fx44YWe6apY+NQ/nHI7OGXV4LNMxD+n+V3AYo7XMbve6sehUJwpytziWzDHSjDAsgFLixlAq89vxYoVZT0YAAFjxrTguRijGRhrzg+Enp85wzH6cZ07d5aB2bAswiIEzFokOD7a6sa4AFAVe/VGjOn0o2vOFYkNzqN/OFfxIekziB87mJp1czvSUp1SMJNNMjIRUKgKmSSw1NhEhI5a9ACCRhVYaAYPHiyv6vVFz5bN4tUjB5YRr3ob0VT9dAt+zQmmEFL1UfCdUoto3759I4SKTiyZVebc6O4gxJjgT7m7TBGjFn/dLQaRF4+gXNSOUZ8/BJRizZo1EcfahKdb3Ak+b/NzTMZFAiGEJF2Y4GoaAa+vvvqq/MFHQactW7aEqoOmGq/uu27BTSjvfscdd0TEQpjAFQJxBquJWevD70IKy87AgQPlFbNu4XFalJs0aeL4GljYbKmuOm6LfbKEiSmOEOei9sPdBVfDY489lqPS6TYrgjk3sKro86LmBqLEZjExM5wgfs2MqWj6CCmc0paVZUwn2gJu6j2hArAtlZwQQjKqwBr81o8++qi44oorZBAssl1UefFUY2Zg2FKLbeIFmUYo766uLOEeckIPSo02WAqBlnD/6HUvpk6dGtp2WpTVVX2szeeczgtXhr5Qux2bU8zz6llBiH1Q8Q85iYz3I0zMBVyNC6LEFCbqfHplXHzXbZV6o8UmNiBkX3755Yh9ejyLH9R7Qg2f++67T9SsWTOHoyWEEH+kxBmGRXLEiBEyFfS9996TwZJBwetq38kv/+GHH1oLZ9nQ627MnTs3KosJBI8ZN6GnBdtcUfB5ur0vLLxewsTJYoLPz8SsKxIvbOnCNtyE0apVq6L6fM0OvCamxcR05dgKvOHzMIWJLn4g9NBI0Wb50LGNyyZ0t23bJqJFzSHm+JJLLonKYkYIITmBUTpRWkycrp69XCGxYgoTBFiaWTV6ZoWtKJZXXREsxl7jj8USE2/MMTgtlm7CBJYmPdsIwkGfT1OYYF7chAnm1o/FRBco+CzM+VbCEBk1qBaLAGUEJbv1OzILA+K7YuvjE0tpeXYSJoSkCgqTKIWJk8Ukmh9yp9gAG7b0U6dKtbYOvmrRc3tfyPzx6sLrJAISFU/iZ46dLDNuPXj0Sq5wqUFYoIYLSs7bPl8EiLqJA91igueawkCdTxciECamJUU9/uSTT4btNwuc6ehtAAAElM3yFUuHZVpICCGpgsIkThaTaDAXumgCE5HNhJL+TtYLvbOuztVXX+16XgSOuoksp8XeDNrs1auXSBTRiL8rr7zS8TEVnIw6HXqTPidh4vb5QJipcdkEjBIg+vcG4sEUJqpWivlabsJvz549EZYzW6YNyuhHKzhoMSGEpAoKkzjFmHjFaOiYi49bnyBkLOm4taXH2J3ShSFa/MZ+2IJ7nYKTTWGCTKFE4WUJ8fs5QpjAErV48eKIx6IVJhCyarG3ZVDBOgIrii5MUP1YT5dXoIieeQ43IWz7HtmEiW4xsbURQME5v24yQghJNBQmcbKYLFu2LLTtJQBU/Q2FW3oxgiDjNXZbETsbtgBaJ8EDsaBaBDz33HOicOHCIlFEcxXv1MdHzbdTd2PUhonVYuIERKspXG+44YaI49zqoPhxCeL5XsLE1kZg7NixomnTpmH72FmYEJIqKEziEGOCDAs9HdOr8h+yHHS86p7Ey9rjx+KAQmxmDAuCMN1A5VFYAJBWmkiiESZPPPGE64Kuuu6an62ZVhuNxcQJuHhMQWuzruBcOREmCHK1jVV35dgEG1KBze+OUx8kQghJNBQmcbCYmCm/XsLEvHrWYxMQ7KgWEregSxtqcXEqc+9nYceVsgoO9VstFRYivX9Mooimaiuq6joBsWFb8Dds2GA91kuYeM0rLBa6OHD7XkyfPt23K8dsjwBhYuso7WUxwfhNkWP2JSKEkGRBYRKjMEHF1muvvVamd5pCxFa7Qsdc6JT5HTVB4O9H0TKIEr0EfTTCRC++Fq3FBFfUtpTTIIAy/orLL7/c83jVO8YE823LjLJ1Y/bKyvFjMXnttdd8xSBNmzYtYp+bxcRvdhWK/3m5uMzGlUGpxEwIyT4oTGJw5SAAFSXhP/jgA9GtWzerhWT58uWOqaF6MTZdmKi4A1z1ot18tFVh1djNhfLpp5/2bTHBwnXTTTfluL9MIoBVBpVI0avm7bff9jwe5eltQJTYrCC2gFQviwlib7yEiVsVYK9CaNEIk/Xr11stJrt37/YUJnpHYghAXQQSQkgyoTCJwWKyevXq0P2PP/7YKkywcGJBq1Kliix6ptwEZkt6YAtYRKGuaLtUKouJGSOiYj9MYdK1a9eIc2DhMhd0rzlJJkiHRmVdt+BWL3cO5tvWRM8mTBA/5FagDNlKbg0So8FmaXOytNjEIiwjZjl6E9Plpnrh6O6/7t27+x4zIYTEGwqTGCwm5hWyraoq3CZwxaCTMOILbrzxRrl/woQJvoQJnh9tN1clTMyrbDVe05VjW4Cw4MOVpHeoRfuAdATv++6775YpzXC7eRW4swkT7Nu3b5+rMPnss88i9jdv3jzq8eqZXV4iwWZJgbXEZjFxS/tW340xY8aE0sr17smEEJJsKEwMvKwUsJiYV7a2wEYcoy90qkrnI4884luY6Auon9LvSpg4BUzeeuutoe2JEydaRZhaHKtVqybHjN4yF110kUhX4EaB1UOPu3Hql2MTJihX71bOHwt9ixYtrPujxdbYz+mztFlS/BTqy5Mnj1WYNGvWTBZ527p1K1OFCSEpheUdowRXqip+xMsFULx4cV/n9CNMvK6EwcyZM0NjtNG3b1+ZmgxBgsDQJUuWRBxTv3790DYCcTMBLL6wAnkJEzP2x8n1poO57NevX0SGjK1IXU5AYC7cgx07dpSuwWgK+unoliOgW//0OSKEkFRBYeIDxGaomARcwcLaoGMLUoUwMYuS6UGIXsIElptoeuqo1wS6RUe3AMGlgSJobguRU0n7dAfF5WBRwoLulLprpkm7fWaqVgpSpStUqBDxmF9hgnH5+ZwRC7Jo0SLRv39/axNAm7C1ZVeZlXpZep4QEjToyvHBSy+9FNr+8ssvIx5HAKyfxnkqzsQmTJSPXwGrh9eCBXeLDT1GYPLkyY7Pt9W0cKrwmgkosRBNbyI3YVKqVKnQYm+6SGzCBPEuJuecc47nGCBEIEoUH330kafFxCaW2rRpE7GPpecJIUGDwsRHZodagMD7778fcQwCXE2wcJhXrAsXLrSWIocw6dGjR8TzvYSJucCqaqZYlBYsWCCLdTmJIcV5550Xdj/agNt0whano7uubOi1TcxYEj2OxMx2scWY2IJK/VhWkCKtA7eRl8XEVvDOJkIoTAghQYPCxAO4Qkzztx8gLGyLh20hsLlyfvjhhzBhcscdd0QcY1bnRPyBom7duqJly5aewbxmpk4s7zVdsPUK8irjr1O6dGnH85nWJ5vgsNUG8RPUbLoOgZfFxGx74PTdoyuHEBI0KEwsYEHPqRUBosQWhGqrVWETJsiOUMIEYsEcE3j00UdjXmSdGg5mssXE9t6imTNTtOn1S/bu3esqQiAA8VqmOPJjMTGzdfB98RImtpLyK1eu9HwtQghJNRQmPhbreFpMbAGWNpcN3DRqPxYz25VtuXLlcmyWNy0qmWwxsb23aIrH4fko7qYCh9H8TmGWgjeLrim3nuni8SNMTDELy42XK8dmidFr0yjoyiGEBA0KEx/EYkWAMIHVw8QWPGsLxjSFiW0BMRfaWMaZTRaTnAoTHNurVy9peVixYkWYWDRTq53Oa1ponBou6hlTlSpVirC+eFlM3FxEei2dWArBEUJIIqEwsaBX20SzvmisCCo7A+Z2t4wYnSNHjngKE1usiClW4nH1m8kxB7bP0cmVY8uggXsGQq569eoRgmLcuHG+sptM4eckTAoXLuxYwwYuQt1CYxOTbsJkwIAB4uGHHxajRo0Sl156qeNxhBCSCjJ3FcoBV111lVxo4NtHk75omulh8UKWjluaqR9hgo62ypJiC9q0LUixCBPTJWBaUDIJm+hyEia6MHCyXJjZTbBybN68WVxwwQWyCJqtb4+ZweXkytH3m3VXIEzQRFK/b2LrJdSnT5+QQHPqQE0IIamGwsQCFme9wZ2Te8MsjoUffCxQWHy8YgBUHAIa7qEaqwn2q3PgdWxN28xxuZVOd0If/zXXXCMyGb0WiMLJGmZzxXilFiNFGx2nEahsCrwXXnjB+hwn0amn+6oGkHrMibnPxFa115aqTgghQYOuHB/YFi9cAZtX2zgumgJlKhDSVm5e72jrtHjBQtKhQwe5DVePLRMjGmHi9DqZgpk5o6woNktTLLE2sLKgH5HNWuHUO8dJwOoxJmbWFiwkl112mWOZ+YYNG1otX5nspiOEZA4UJj6wLVKIDTCvqmEB8Zt+CiGh4gu8LB02iwmuiDGukSNHyu6/6N8TS0bNk08+6VorJZMw06v1NF6TeAQBo/cOzoMmiFdccYX1GFsaOMSmm4iAxUS3qFSpUiXscZswAszAIYSkAxQmPrAt+LCMmMIEV7J+hQkWHr/WFZsw+fzzz+VVMYIckSmCgMxY6NSpk3j55ZfF1KlTw67CMxHMkwnSeG2xI/FIm0bHXjR4RLNEp0J36OR71113he2DmHETJvie6XElZnE304KioDAhhKQDFCY+sP2gQ1SYIgSLkF9hgnP6dZ3gOL0vDjoD24IzYwHjgKWkXbt2ItOBJcFsXAjrgxmoavvMzbokfoELx6v67gMPPBAhTGwxRQrEJOnCxCw/X69ePWulWrpyCCHpAIWJD7CwmAuVzZXjlGETD4sJsjQQXInuwHDdkNgwXTQQJrbP0dxXpEiRhI3JTO3FGPUYI5P58+eHxaaYFhNVddYsZU+LCSEkHaAw8Ylp2re5chAvgLgCE1Ut1FwkbMIEqcomapFC/5v77rvPV7VQ4k+Y/PXXX1Zh0qhRoxyX+7dhVusF5ut7uXJUOrkC6cm6iFVjrVGjRthzKEwIIekAhYlP9IUAbNmyJWKxaty4sRg6dGjEc+vUqROxKGDhsblybCmpGzduzMHIiVvTQidhAheM3ggvXov6rFmzQtv9+/eXt6arZ9++fZ7nwfcPIM4IFpJBgwaJMmXKhBX1MzOBzG7XhBASRChMYgRlyc0FDVYVW60IuAtsVVptFhPEO6jqsfp5SXwwF2fEjpif45133ilv9TiPeJXqr1y5svw+rFu3Tjz11FOO4sUrU0vVvsG4IE6QcbRt2zbRqlWrsOMaNGgQ2o5XXBIhhCQSCpMYKVu2rFWY2BYwXBGbpnkniwncNOYCksn9a5KN2RCvQoUKEZ9jjx49Io6NpxsEnz1SfJ2q7OL74te64fXdQLZVv379xJw5cxzL3xNCSJCgMIkRVPK0FVgzwTG1a9f2bTHB8aYwsVXxJPERJkiXNoWJWsBVEKleUj4Z2ISJLU4JeKWJwwL37LPPWmOXCCEkiFCY+ERfpED58uWtFhObyd2W1eMU3Ag3jil4nnjiiRyMnOjoC37Tpk2lxcG0XKn7yH6CqwdZM8OGDUvaGBF7gtgXHXwnatWqFXFsrPVrCCEkqFCY+OTjjz8Ouw+xYcumAOPHj5ePoT6IEiumMMHCs2rVqojXQeaN2bTNTAcl8Ql+VTEk5ueohAncdXv27JENGW21TuKJ/hmjEBxiUXQgjoYMGRLxvESmMRNCSCqgMPGJ6U6xFVhTIqRLly6yngkqqjpZSDZt2hRR8RNX54g7+P3330P7zIJgJGeYlgg3YeKUFp4I5s6dK783EMAQuHoTSZVhg4JpZqZNMsZGCCHJhMIkCmbMmCFdLe3bt5eLhFuMiS2mxATpxTZ0YcL4kvjy66+/hrZV9pO5uKeiQioq+8LS1qRJE+sYVNyLOdZ41VchhJCgQGESBdddd504dOiQmDJliry/Zs0a32m9tit1MytDpYjqwoQLT3y5++67Q9uqM3NQ5xgVXmEhQQNABFDbhIlZX4cQQtIdNs+IEv1KFtaMd955x5cwMdvbqz4m3bt3F2PGjAkLzNTb3Ad10UxXevfuLWN40DxPdfbVA5ubN28uggKaKmKsiGdS8TDm98EmeAkhJJ2hMMkBiBFBxU0/wsRMDZ4+fXpE1U9aTBIP4kfM6rzIdsHniIqrTkXPUh2sq4TJhg0bwh5nNVdCSKZBYZLDbrU6bsJEL26FHiaqj4lNmOi1NhjcmHhU5dR0RO8yTAghmQBjTOKIWxVOvQW9nhqqp6/arn5pMSFuNGzYMNVDIISQuEJhEkfcypaj+ibiU3B1jm2bMFEWkxtvvDGsrgkhTiAwlhBCMgm6cnIIxIYKQHRzu6AeCZqswU2Dwl0Ks7OsKj+OmAJUl7366qsTNHJCCCEkeFCY5JBFixaJAQMGiNatW1t73+jY+q0UKlQotF2qVKlQNc+33norAaMlmZBVhFL5hBCSqdCVk0Muuugi8cknn4Q60kYLyo8jXRWBs2+//Xbcx0cyC7NaMCGEZBq0mKQYZOt8//334rfffpP9UAhxQ8/uIoSQTCSuwuSHH36Q8RHffvutjJG48MILRb9+/ULuCtTnGDx4sKxoiXLgsBawHfv/pRm7pRoTYpbRJ4SQTCWurhyUx27UqJF47733xJw5c0ThwoXDCpC9+uqr4vDhw2L27NnimWeekdkpEDOEEH+wrg0hJNOJq8UEJdr1pnNt27YVt9xyS+g+BAnECMzRaFrWoEED2U31tttucyweZRaQQhaMW72QWFGpuuqWuMP5Si5qnmGJRPPHzz77TMabcP4j4XczPnAe/cO58octCzXpMSYrV64U5cqVk9tHjx6VfT8qVKgQehzbZiM8nQkTJoT6yCjatGkjBU+i2LlzZ8LOnYlwvpI/3yNHjhQbN26U4n779u2pHlJg4XczPnAe/cO5ckcvlZESYYIP6OWXXw71HkFwJ9BTarGtN6wz6dKlS6gDbDIsJhhzyZIlfau6bIbzldr5rlSpUqqHFFj43YwPnEf/cK7iS1TC5NZbbxWrV6+2Pta1a1dxxx13yO39+/dLM3PPnj1lgzTVPA38+uuvocwCbLv5zCFAEiFC3MCXil8s/3C+kgvn2z+cq/jAefQP5yoFwmTcuHGexyC4FQKlVatW4oYbbgjtP/PMM2XTuy1btojq1avLfVu3bpXVTQkhhBBCEpKVA0tJvXr1ROfOnSMeb9asmRg/fry0lKxdu1amDTdt2pSfBCGEEELiH2Myb948GZSHgLx33nkntP+rr76St8i+efLJJ2XtElhQUOOkTJky8RwCIYQQQtKYuAqTFi1ayD8nTj31VClMCCGEEEJsMEqHEEIIIYGBwoQQQgghgYHChBBCCCGBgcKEEEIIIYGBwoQQQgghgYHChBBCCCGBgcKEEEIIIYGBwoQQQgghgYHChBBCCCGBIdfx48ePp3oQhBBCCCGAFhNCCCGEBAYKE0IIIYQEBgoTQgghhAQGChNCCCGEBAYKE0IIIYQEBgoTQgghhAQGChNCCCGEBAYKE0IIIYQEBgoTQgghhAQGChNC0oxrrrlGrFq1KtXDIISQhJBVwoQ/6NHzzjvviA4dOoiLL75YvPrqq6H9y5YtE7Vq1RL169cP/a1cuTKlY03n72XdunVD89i0adNUDylt4Pcz9u9cvXr1xLFjx0L7fv/9d3HZZZfJx4gz/M4lnhOT8BokjTnrrLNEjx49xEcffRTxWPHixcWMGTNSMq5M4+WXXxbVq1dP9TDSDn4/Y6dw4cJi3rx54uqrr5b3sY35/PPPP6M6z19//SVOPDF7lhJ+5xJPVllMFGvXrhUdO3YUDRo0EC1atBBvvvlm6DEo4IEDB4oHHnhAXj106tRJ7N69W2QrDRs2lPOUJ0+eVA8lq/jxxx/F3XffLa644grRunVr8fXXX4c9vnr1anH99dfLx1988UXxzz//iGyE38/YgWVuzpw5ofvYvuqqq0L3x48fL38fMb9dunQRmzdvDj0Gq8qkSZPkd7NVq1Yim+B3LvFkpTCBuh8wYID44osvxJAhQ8To0aPFxo0bQ49jf5s2bcTcuXNF6dKlxWuvvZbS8QaVffv2iSuvvFL+MI0ZM0b8/fffqR5SRgCR0adPH1GnTh3x8ccfi0ceeUT+HTx4MHQM9uN7CVEN0TJz5syUjjmI8PvpzkUXXSS2bt0qfv75Z/m3ZcsWUbt27dDjZcqUEa+//rr4/PPPpdvi0UcfDXs+fidxITdt2rQUjD6Y8DsXH7LH/qZx7rnnhrarVKki/fu4AlX78c+Jf1rQpEkTMWrUqJSNNajgR2vq1KmiVKlS4ocffhD9+/cXp512mrj55ptTPbS0pHfv3uI///m/6wR8D//44w/Rrl07ef/8888XF154oRQgyv9/0003SZMyaN++vfj0009Fy5YtU/gOggW/n96ccMIJ4vLLLxeffPKJvI9t7FPgvgIWk3HjxonffvtNnH766XIfvp8FCxZMwciDCb9z8SMrhQmuEoYNGyY2bdok/an/+9//5JdKUaBAgdD2qaeeKv8ZSThYFNXCWK5cOXHrrbeKt956i/+EMTJixIhQjAlEBtyJMBkrcOVVuXLl0P0iRYqEbR84cCDJIw42/H76A/Elzz33nDh+/Li4//77w1yC06dPlwstrAC5cuWSxxw5ciQkTPTvIOF3Lp5kpTCB+6ZGjRri+eefl8IDbh3805HYUVf7JOcUKlRIlC1bNiz2yQSLhb6tfhCJHX4/7cBiDLEBzjvvPPHtt9/K7T179siLN7gLYcHDxRuyTPTfSYgV4gy/c7GTlTMHC0ju3LnFKaecItO5Fi5cmOohBRZE3MOtgCspXLVjG7dIjUOAJtixY4c08yJYmOScqlWryvlGWiIsevjD91TNN8CVGGJOYCl54403pF87G+H3M+cMHTpU/pm/kVhY8+fPL+dTT4vNdvidSzxZaTHp1auXGDx4sLwaQFAXvzzO4J8LQVx6pD6C4HCV9fDDD4tffvlFur6aNWtGk2Ucg7OHDx8uTeyvvPKKvErFle2DDz4YOqZx48aie/fu8nPA3Gdr7Ql+P3MO3A4mFSpUkFlfiCNBnES3bt3ESSedlJLxBQ1+5xJPruNZ5MNAaiW+VHo8CSGEEEKCQ9a4cmBmgwY7++yzUz0UQgghhGSzKwdum8WLF8sgV8SVEEIIISSYZJUrhxBCCCHBJmtcOYQQQggJPhQmhBBCCAkMFCaEEEIICQwUJoQQQggJDBQmhBBCSED43//+Jx577DHRvHlz0aBBA9G5c2exZs2a0OMTJ06UBRbRZBGFGPX8lXXr1smieGhM26NHD7F3797QY7///rssAIeCojj3Rx995DgGtCRAI1ucQ2fFihVy/6BBg0QioTAhhCS8hhB+zPCHHzxCiDN///23KFasmCwG+sUXX8hO4n369JFtAhYsWCCmTZsmxcnbb78tO46///77IUHTr18/KUzmzp0rLrjgAilEFGgrcPjwYTF79mzxzDPPiGeffVZ2QXYCnaZ/+umnsFYYc+bMkd2TYy3l7xcKE0JI3MCVlHmlhb5U6P+Dv5NPPjml4yMk6Jx22mmy3UTRokVlv6KmTZvKdgDbt2+XoqJVq1aiRIkSsnEnSt5jH1i+fLk8rmXLlrJeF7obb9iwQezevVs+juOwD/+P1apVk9aYjz/+2HUs6MGljkHPrq+++kpaanT69u0rj8P+Bx54INQUEhchaPmCnl/oYg0rkF8oTAghCQXdaXGFhz92QSYkOnbs2CGOHj0qSpYsKbZt2yYqVqwY1tNo69atcvv7778Pe+zUU0+VAgb78Xw0/cTxtuc6AUGhXD5odgsrDISNTqNGjcTMmTPlH6w6Y8eODbP+bNq0ScyYMUM89NBDvt8zhQkhJC6gkeCsWbPCfNH4s7lylGVFPUf504cNGyZ94bjFfezHFZfO/v375dXXVVddJS655BJx3XXXyR/DaEzFhKQDv/8bF4I4EwgCLPxnnHFG6HFsHzt2TG7jVn9MPY7n4E/dtz3XrcFjrly5pICBxQVCxQTNCmHlwfjat28vVq1aFfY4rKew4EAo+SUrStITQhLPOeecI3/o4MfGj17ZsmXl/o0bNzo+58CBA9LfDUvKr7/+KqZOnSrbR8C3jR+6ffv2iSFDhoiaNWvK8+Hc+JHGfvUauCJEF2aYrNHllZBM4K+//hL9+/eXlhK4dsDpp58u/08U2IYoALjVH1OP4zn4U/eVxUN/btu2bUOBsohh0YEYwcUBAnDR3kWPS8EYEYCLWBh0VUYgbr58+UKPwxUVi5WUFhNCSFx47rnnRL169UIiRblv4MpxAn7rl156Sbz33nuiSJEict/OnTulQMEPJK60/vnnH+k/Bwj4gygpWLCgNA/jOATxAVhe8FxC0p1//vlHWkpgrYB1EbcAQnzLli2h42DJKF++fMi6oT8Ga8uuXbvk/jPPPFP+zzg9F/9XiB/BH2JbdJo0aSL/P+vXry9jWHTg5sH/5vjx48X8+fPl/6KeJaTGHS0UJoSQlIEfzOrVq8srK/WDiB9LZCXgKi9//vxy36FDh0LpkAD+cgTcwR10//33y334QVy7dm3K3gsh8eKpp56S33FYE0888cQwtwlEAgQHHp8yZYrcB2BV/OOPP2SWDjJ0IBYqV64sihcvHnou9sFSgv8TCAkE1nqBCwZk9Nx2220Rj+FcCGjH/zGsma+//npc3j9dOYSQlKH7vJGeaO5TV1xmr1HdVaQTjR+bkCCyd+9eaQ2EtRD1ShQjRoyQFsnWrVuLTp06SasKMnAQYwUgEIYOHSqeeOIJ6f6sUqWK3FZAWDz55JMyNgtCAqnFZcqU8TUmXDzYQAwYgmJhVSlcuLAcz1tvvZXjOaAwIYTEDSUMYEZOBPixxQ8hRAyuKmFZUVdu8HMjQ4CQdObss8+WAeNOdOnSRf7ZOO+888Sbb77p+L8JYeIH/F8tWbLE+hhivBSIV4Fg0kEKs9c5vKAwIYTEDXUFtn79enHjjTeGajLECwTpwVSN4NgbbrhBWk0gShB3gkC8Fi1axO21CCGpgTEmhJC4ce2118pCS7iSQnAdfNkwOccLxJxMmDBBphnnzZtXvgb86jVq1BD33ntv3F6HEJI6ch03nbeEEEIIISmCFhNCCCGEBAYKE0IIIYQEBgoTQgghhAQGChNCCCGEBAYKE0IIIYQEBgoTQgghhAQGChNCCCGEBAYKE0IIIYQEBgoTQgghhAQGChNCCCGEBAYKE0IIIYSIoPD/AXxmlDt7wBvCAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "n_points = 2000\n",
    "\n",
    "kwargs = {\"freq\": \"h\", \"length\": n_points, \"column_name\": \"data\"}\n",
    "# Generate a time series with trend, seasonality, and noise\n",
    "trend = tg.random_walk_timeseries(**kwargs)\n",
    "seasonality = tg.sine_timeseries(value_amplitude=5.0, value_frequency=1 / 24, **kwargs)\n",
    "noise = tg.gaussian_timeseries(**kwargs)\n",
    "\n",
    "series = trend + seasonality + noise\n",
    "series.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "192981ff-027c-4ddf-967a-ebe66fab249d",
   "metadata": {},
   "source": [
    "Now we generate categorical labels based on the recent behavior of the time series. We'll create four different categories based on the volatility (standard deviation) and trend direction over a 24-hour window. This simulates a real-world scenario where we want to classify the current state of a system based on its recent performance.\n",
    "\n",
    "**Important**: The labels generated here are artificially created using the same patterns that we'll later use as features. This is done purely for demonstration purposes to show how Darts' classification models work. In a real-world scenario, you would have actual categorical labels that you want to predict based on time series features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a86e67fa-848e-46a2-a502-aa67017d5f6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate labels based on recent behavior (last 24 hours)\n",
    "cats = {\n",
    "    \"high std, trend inc\": 0,\n",
    "    \"high std, trend dec\": 1,\n",
    "    \"low std, trend inc\": 2,\n",
    "    \"low std, trend dec\": 3,\n",
    "}\n",
    "window = 24\n",
    "series_vals = series.values(copy=False)\n",
    "\n",
    "labels = []\n",
    "for i in range(n_points):\n",
    "    if i < window - 1:  # Not enough history for first 24 hours\n",
    "        labels.append(np.nan)  # Default to normal\n",
    "    else:\n",
    "        # Calculate features based on recent behavior\n",
    "        recent_values = series_vals[i - (window - 1) : i + 1]\n",
    "        recent_std = np.std(recent_values, ddof=1)\n",
    "        recent_trend = recent_values[-1] - recent_values[0]\n",
    "\n",
    "        # Define labels based on recent behavior\n",
    "        if recent_std >= 2.5:  # High volatility\n",
    "            if recent_trend >= 0:  # Increasing trend\n",
    "                labels.append(cats[\"high std, trend inc\"])\n",
    "            else:\n",
    "                labels.append(cats[\"high std, trend dec\"])\n",
    "        else:\n",
    "            if recent_trend >= 0:\n",
    "                labels.append(cats[\"low std, trend inc\"])\n",
    "            else:\n",
    "                labels.append(cats[\"low std, trend dec\"])\n",
    "\n",
    "labels = TimeSeries.from_times_and_values(\n",
    "    times=series.time_index, values=labels, columns=[\"labels\"]\n",
    ").strip()\n",
    "\n",
    "labels.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a5c6fec-51ed-4c92-a621-ea152481f741",
   "metadata": {},
   "source": [
    "Next, we create features from the time series that will be used as input to our classification model. We compute rolling statistics (standard deviation and trend) over 24-hour windows to capture the same patterns that were used to generate the labels. These features will serve as our future covariates, allowing the model to learn the relationship between the time series behavior and the corresponding categorical labels.\n",
    "\n",
    "**Note**: Since we're using the same patterns for both label generation and feature creation, the model will naturally perform very well on this artificial example. This is intentional for demonstration purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4d62dd3a-1e9a-4ac9-8118-774e8b891676",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get rolling std and trend over 24 hour windows\n",
    "window_kwargs = {\"window\": window, \"mode\": \"rolling\", \"min_periods\": window}\n",
    "features = series.window_transform(\n",
    "    transforms=[\n",
    "        {\"function\": \"std\", **window_kwargs},  # rolling std\n",
    "        {\n",
    "            \"function\": lambda x: x[-1] - x[0],\n",
    "            \"function_name\": \"diff\",\n",
    "            **window_kwargs,\n",
    "        },  # rolling trend\n",
    "    ],\n",
    "    keep_non_transformed=False,\n",
    "    treat_na=\"dropna\",\n",
    ")\n",
    "\n",
    "labels[:100].plot()\n",
    "features[:100].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9c3ac66-d3c6-4106-bcb8-0ceaa5460713",
   "metadata": {},
   "source": [
    "We split our data into training, validation, and test sets. The training set will be used to fit the model, the validation set for early stopping (and optional hyperparameter tuning), and the test set for final evaluation. This ensures we can properly assess the model's generalization performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "29cbf6b7-b014-48b0-b1ec-75c3645a355f",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_train, labels_val, labels_test = labels[:1000], labels[1000:1500], labels[1500:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bcfabb6-b4d9-407a-8c42-c4372e98a9ea",
   "metadata": {},
   "source": [
    "### Classification Models\n",
    "\n",
    "Now we create and fit one of Darts' classifier models. For convenience, Darts ships with a couple of classification models: \n",
    "\n",
    "- **SKLearnClassifierModel**: wrap the Darts Model API around any sklearn-like classifier model (default: Logistic Regression)\n",
    "- **XGBClassifierModel**: wrapper around XGBoost’s XGBClassifier\n",
    "- **LightGBMClassifierModel**: wrapper around LightGBM’s LightGBMClassifier\n",
    "- **CatBoostClassifierModel**: wrapper around CatBoost’s CatBoostClassifier\n",
    "\n",
    "Let's use `CatBoostClassifierModel` and make it use the features at time `t` to predict the label at time `t`. To do this, simply set `lags_future_covariates=[0]`.\n",
    "\n",
    "For more information on lags and Darts' SKLearn-like models, check out [this example notebook](https://unit8co.github.io/darts/examples/20-SKLearnModel-examples.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ba7c9891-6fad-4373-881b-890ba6697d00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CatBoostClassifierModel(lags=None, lags_past_covariates=None, lags_future_covariates=[0], output_chunk_length=1, output_chunk_shift=0, add_encoders=None, likelihood=classprobability, random_state=None, multi_models=True, use_static_covariates=True, categorical_past_covariates=None, categorical_future_covariates=None, categorical_static_covariates=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = CatBoostClassifierModel(lags_future_covariates=[0])\n",
    "model.fit(\n",
    "    series=labels_train,\n",
    "    future_covariates=features,\n",
    "    val_series=labels_val,\n",
    "    val_future_covariates=features,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "543cdff9-ff7d-4a15-b334-6bee9a398726",
   "metadata": {},
   "source": [
    "### Time Series Labeling\n",
    "\n",
    "Great! Now we can apply the pre-trained model to label the entire test set using `historical_forecasts` with `retrain=False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b43b7c7f-74ac-4d49-811a-b2485a328735",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3iNEDEeIK/+4YxIQAEGKKLjejwR0DeIzVxdnULIsJQXZMuECEuKI4KBETAkBSYkLgjgF2lhDLVXQRmBoqECGq3TFQIQBEEhMiFFLEaVjzGKdiq+wYDJVwgQhxBeqEAJCMiqncY37dyXCbBBJRJ4SNHdklEJaQMIEI8WQJcZkdw+2HmBAA1IJiZcB3nRDe9eIj+xFUDkSIG4qTmVt3DDeYoUEAiMgSIsSE4O621jG5Y4yBqfvmbQSmhgtEiBJ3TFmE5JnNDwAQ0tox8gXscDMA5GvHpCTzNkRImECEqAhM5VbbxeQHQFSWELhjgHV8nhCYyub2ffM7LCHhAhGiJEW3vB+yYwBQS8FFxVQAjPF5ojuGzdn7XofrLlQgQlyR8WQJSXGWEGMwFAAgHEuIUKwMp2HNY7wfFLJj9LkdgyQKIEJcoKWK3ZT3YQlR0iQAgCw7hhMeQmBquE0CiVtFl83tra/DHRMuECGu8JYdk+LECiwhAKiFP8WEtWMy5ekN2TFAVrbd69wOggciREF2TIq3hECDABB9im64TQJJsIRofLEy3h0DwRomECGu8Fq2HYGpAETtjkGxMsBjnIr1hJiS4GCP4I6JAogQN6T8W0LgjgEg+rLtMIUAWXZM0eqhz9mlgQQREibZUL+tVrJjuJgQGEIAUIyLBexwGgJzdkyqosVJQTDAEuIGj9kxgiUEKgQApfBnGG/9SCNFF3AYrdLsaUmEcDEhcMeEC0SIG1IeLSEpBKYCEEnZdotiZbiwANnaMYI7BoGpkQAR4oAYWOo9JgRrxwCgFj79VihWhsBUYLd2jMa5Y4S5HSIkTCBCHMjluMHpOiYElhAAIi9WBncMsHPH6NN0OTsG7phogAhxICdYMtyJkDTnjkF2DABhZsfAHQPcu2OKlhB241i+ecRYCROIEAeaW/KeLSG8CEHZdgCiXzsG6THAnKKb4i6BZUsICBeIEC/uGLfZMbCEABBNYKqwdgxSdIGdJaQcE8K70GEJCReIEC/uGJeCIs1NeYgJAUAt/CnGrx2TzSAmBNin6MqyY+C6CxeIEAUxIYIlBCoEgPCyY/iy7XxgKi4sNY/MEsK7Y4pl22EJCReIEAfyOc1HTAgsIQBEUqxMWMAOMSHAoVjZvkKUYp0QECYQIUqyY8qDGTEhAERUrEywhIBaxzgVt1rQiu4YIdE73IbVOBAhqrNjMPsBoBbNomx7Su6yAbWJMVOx1R2zrxq2fuMId0wUQIR4qbKn5b1bQqBCAFCKcA/LL2DHqxBQ8xit0roI4dwxRWsI4ofCBSJEhTsmjZgQACKpE4IUXWCBcS5uHTdFEQJLSFRAhDiQrzAmJA8VAoBS+DtXITBVKNuOC0utI64DVhwTvAgpgrESJhAhDuQEd4yPwFSIEADU4mYVXZyGNY/JEkKcO0aP44MlJAogQhRYQjJwxwAQTYou6oQA1ym6TITsC0xFnZDIgAhxoLnF+yq6vAhBii4AamnNcmgFdUKAFcbpO8+NmxSfHcOPG6AciBAV2TGwhAAQHlZrx6BOCOAoGLNjChlhqQ1kx0QDRIgDuYKf7JjyY1hCAFALf4ZlM2np2jEwsQPjVFzQ0nJLCMZKqECEeLKEuHTHCBVTVbQKACBN0eWuH0JMCM7DmsdWhMBWFhkQISpSdNPWVfoAAMHCm88FdwzqhAA7dwyJlhAEpkYDRIiCFF0EpgIQIoIlRB4TggsLMK8dY2UJwVgJE4gQB3I5Hym65XgnBKYCEFGKrmAJwXlY85jdMVkxJqTkRocICROIEE+WEHfZMVxsHCwhACiGr4ZqVTEVFxZgvCEsFBev21dgEu6YaIAI8RKY6rpYWfkxNAgA4SGm6Ja34zQEpmJlnAhpzY4pPQmzWTUPRIgDwtovrmNCLN4PAFDrjhHKtmPtGGCdJCCIEGEBO1wWw8RTbzc3N9Ott95KZ511Fo0YMYIuvfRSWrx4sXTfPXv20E033UQnn3yyvv+cOXMoifgr2+5ZtwAAgnDHWJZtB7VOwcESAndMNJQjc1yQz+epV69eNH36dOrevTs9//zzdN1119EzzzxDbdu2FfZ94IEHaOvWrTR79mxasWIFXX311TR48GA66KCDKEm05HwsYMen6MIfExn3/f5Nmv3yltLzcWd2p/EXHBZpm4BaLGNCYAmpeUyBqan2Fh4YjJXYipDGxka64oorSs9HjhxJU6ZMoaamJhoyZIiwLxMfkydPpvbt29PQoUN1y8ncuXNpwoQJUgsL+xMals1SfX09RU0uxwej7hMh+R1EmfIANsL7F9ny0QUUC3FFsZ+C6K8n535AV08/Qtg25/08DR6wmo47shfVOkH2ddSIHk/ufOOuOlqEv7Wa+jru2PW1GN/HTNacCOEXsEulcKwq7Os0fycepAgxsnLlStq+fTv16dNH2M62bdq0iQYNGlTaxh5buW5mzpxJ06ZNE7aNHTuWxo0bR1GzafMWsyXk3fOIjphX2pzOfUKHd3+LFm8+k9rl3yLK7y69tm37dl2kAfesWrWq4s+Y9fxyIvqMuDGVoSfnvE89u7RU/PnVQhB9HTV5Lo1+3bp11NSx9YZmw6ZdRDSk5FaN+jyshr5OCrK+/nTHDsv9P3vQFnrxvQ77nqUiHytJ7+v+/furFyHFmA8WF8KsHTy7drGTn6hdu3albezx7t3lizPP+PHj6ZJLLomlJaRTp83cs1al3KPNezTj1uXUu2cHWrJ8Cx07tAf16fUFevDp9+ic0w+h8T/cWnpH+/YdqF+/fhG0PHkwRc0GNBO1XpS0jHbtVpef5LcTZTrqDzt16oTjEXBfR006s65kpDzwwF7Ur18P/XFj+53cPpnIjns19XXcsevrdm1Xlh73qn+Vvnpmq9tl8IAO9D/njaLOp7y379UU5ogQx7UvEZLL5WjixIn6l/PumSLF+JCdO3eWBAp7zNw5MpjYiIPgkCFG1beKkFQqRWNObbXyHDGkdcJjfP3Lh0vrE2Di8Qbrr0r7jB2j0mOtmQtMxPEIuq+jp3ys6+sypd9TX8dNb1r0x706+joZSPuamxNOPzpHk284Wf7mFI5TmOM67Uf9MAsIm+RvueUWYbIv0rFjR+ratSstXbq0tG3ZsmU0cOBASnZueVmE2CHIFsSlRgIfEJzispqQMV198OeYWCcE2THAYpxIp3CMkijwLEImTZqkx3vceeedusvEijFjxtCMGTN0C8g777xD8+fP1wNZk4Zw0do3ip1UHz/AcdGLBlH8lUUIKtjWZp0QZDwAJxHCb0Jganh4csesXbuWZs2aRQ0NDXTGGWeUtk+dOlUPCGMBpo899pi+jWXB3HbbbTRq1CjdMnLDDTckLj3XtyWEzwzERS/6CUcQIdG0B6hEbv2AJQRYWkdlU7iQ1SiWWgAxESEHHHAALVy40PL10aNHlx63adNGFyFJRxTEfkSIooYBW0QLFOeOwQ1O1aG5WUUXdUJqHtESknKwhGDiDgtoPeWWEFUtA26Pm2gJwQGpPmAJAV7FqnSP0iMUmQwPiBCPhZDciBD+bgyDORrEbucnlyhaA0KLCeFmNMSEAP8xIZgowgIixAHhzlnzbgmB+T8OMSFc5UyIwpp0x2ABO8AvJuoUE5LjCuABtUCE+LCEOIHsmOhBYGotIV+0jreK4LADK7EqtYRgoggNiBAHNEFFFFzdTQuTHwZzJIiTCNwxteOO4UUIP73BElLrCCJEcuXjLaZwx4QHRIgDed4q51JQ8CobGiQaeDeYMLkYF7ECiYd3tcjucAFwkx0jpuhinggLiBAf2TFOIDsmbhMOXzEVB6T6sEjL5RadZGukgtrGKTsG7phogAhRHBOCsRyv7Bgcj+qDP6TZjHFK2ydAEZha8/DjxKlsOywh4QERotgSAkUdh7VjIEKqGs3OEtJ6wHHYAVJ04wlEiAOiBnErQhATEtcUXYjC2glMFV+FJaTW4W8o004xIZgnQgMiREHZdqFaNMZyrGJCcDyqEbvAVBxw0Apv3IAlJD5AhDiAwNRkAhFSO7ixhCAwFTi5Y4TYMYiQ0IAI8RngaAdESJzdMdG0B0QUEwJ3DHBbrAyxfJEAEeKAMBh91AnBYI5DJDzKttesJQTHG8huTKTFyspguY3wgAhRbAnBYI7b2jHRtAdEGxOiaZjqah0xJkRmGePWjhGqVAKV4Mx0AO6YZMJbPERLSEQNAhFnx4Bax7FYmXDziHETFhAhntwx7taOyQird6prG3B514MU3RqKCTG9uO9fxITUOuLaMbIF7DBPRAFEiI8UXU+WkMBbBDyvHSOsCRFNe4A6eIEhLlrX+ioAXi0hyI4JD4gQB1C2PZkgOwaIwBJS64jB6vYxIZgnwgMiREGdEHEVXYzmyOMEBFGI41G1lhAtb3ottW/tGLhjgOiOMb+OYmXRABGiOjsGYzkGhYkQmFobMSGSg1s64BAhtY5jsTIEpkYCRIgDQgyB67VjPL8FBAxSdGsHzfbg4oADmQhBYGpcgAjx4Y5xXjsG2THxCkKDCKlubCwhyI4B+0BgajyBCFHhjuF6FRe9OPh/cYdTG9hZQiBCah3nwNQy0CDhARHigJ9rFj/AMZbjVrY9kuYAhZStHDi4oBJLCJ/Kj7EUFhAhDghj0fXaMZ7fApTGhMi3g9pxx8ASAsS1Y2QxIWVgMQ0PiBAFKbpYOyZ6EBNSg0gObjHYUMNUV/MUHCwhPPk8JoqwwJmpJEUXgalRgxTd2sHeHYMDDlxmx8AdEwkQIQpEiOCOCbxFoBL/L8ysNVYnBO4Y4DYmhHuMeSI8IEIcEL0xLhewy8ASEq9lu7F2TG1gfbIhRRdoFnO01GIKS0hoQIQ4kK8wJgQiJF53PTgeteqOgQipdZzdMeXH0CDhARHiAGJCkgliQmoJaxFSXDsGAE/uGKiQ0IAIcUA0hCBFN/EL2EXRGBCKJYQvu20GlpBaR6gd5KBC+JiQU045ha699lpX3/Hiiy/qN6Fbt26tqK0HHXQQ3XvvvVQLQISotoQE3yTguSaAfDuoFoqr6MIdAyqxhCAmJAogQnyIEOe1Y6zeD8KC7/eM4I7BAale7OqEQITUPIgJiSUQIUrWjkFMSKzcMdwox+RSfSAwFVQyJ3iJCfnjH/9IxxxzDHXo0IF69uxJX/nKV2j9+vWm/V5++WU64ogjqE2bNnT88cfTO++8I7z+0ksv0UknnUSNjY3Up08fuvrqq2nnzp3ydmsa3XLLLdS3b19qaGigXr166ftXCxAhDogaBDEhSQHZMbUE1o4BlWfH8CrEymLa0tJCP/3pT+mtt96iWbNm0UcffUSXXnqpab/vf//7dM8999Abb7xB+++/P51zzjn6exnLli2jUaNG0Ze//GVavHgxPfroo7oo+fa3vy39zj//+c80ZcoUeuCBB+jDDz/Uv3fo0KFULWSjbkDcQUxIda2iCxFSa8ASEmeYVWHdunWBfmY+n6dMJmPavn7TXqJCg/540ppOdOVF71rGhFhZTL/xjW+UHg8YMICmTp1Kxx57LO3YsYPat29feu3mm2+mL3zhC/rj3//+99S7d2966qmnaNy4cXTHHXfQJZdcUgp2Pfjgg/XPGTFiBN1///269YRn5cqVutXljDPOoLq6Ot0iMnz4cKoWIEIcECvnebeEoDhWHFbR5bZDhNRYim5xG4y+cYQJkDVr1oT+vdu3tlolyGKeyOflE/eiRYt01wizhGzZsoUK+yZ4JhQOPfTQ0n4nnHBC6fF+++1HhxxyCL3//vv6c/ZeZgF58MEHBcsL+6wVK1bQkCFDhO8cO3asninDRA+zoIwZM0a3rGSz1XH5ro5fEeeYkOCbBCpxx0TSGhB5iq5DMDmIBnaHHzRWlpBPNu2l3D5LSMfOnRxiQsyfy2I2Ro4cqf8xAcHcLEx8sOfNzc2u28esJhMmTJDGdfTt29e0jcWM/Pe//6V58+bR888/T9/61rfoZz/7Gc2fP1+3jCQdiBDVa8fgqhcNgjuG24zjUYU4B6YiOyaeLFy4MNDPY9aEpqYm6tevH6UN0adHnP8ivb3pZP3xrde0WiW8xIQsWbKENm3aRHfeeacuDOza/+qrr5YEBbOYfPDBByULx9FHH03vvfceDRo0yPXvYgGszPrB/q666ioaPHgwvf322/pnJR2IEBVrxyA7JrbLdsM9Vs3YuWMgQmodIW1fsnaMuNCl+f1MVNTX19N9991H3/zmN/WMFxakKuMnP/kJde3alXr06EE/+tGPqFu3bnTeeefpr9144416xgwLRL388supXbt2uihhVo5f/vKXps/63e9+p1t3jjvuOGrbti396U9/0kUJE1rVABylDogXLbhjkoJWWlkVlqlqR7OdxnDAgSxOzH4BO1mKLnO/MEHw+OOP6/EfzCJy9913S7+LvXbNNdfQsGHD9LiXZ555RhcwDJa6y1wpzDpy0kkn0VFHHUU//vGP9dRbGZ07d6Zp06bR5z//ef29zC3DPo+JnGoAlhAHxKEId0zSawLgcFQjxZgQs5kLlhAgvTGRlEzlt/DWblaKvcjFF1+s/4mfK5Z4Lz4/++yzLdvCMmqee+45y9dZ6m8RZkEpWlGqEVhCFKwdI6hsXPUiAXVCahGsHQO8rydVBBVTowEiREVgKu68Y1YnRL4dVAs2AqNkYocIqXUci5Xx+0KFxNMd88QTT+gFV5YuXaoXbWFpRjJYZbcZM2aUfGCMBQsWUBIRg1DdrR0jFCvDWI6cDERIleOmTghESM1jcWNSBJaQBIgQFuF75ZVX0pw5cxz3Zf6wm266iZJO5Sm6mPyij4SXbwfVAeqEgGACU62KVILYiBAWdFNcnCdIWKEXY7EXVg2Ot6REhaCIuYFZrJQnR1y11X5fUKTYT0H0l2WKLo5H4H0dJ0uI+feULSFR/dbq6ut4Y9fXxhtK4z68iGUVU3G8/Pe1sUZLJNkxf//73/WoYpYnzXKhTzvtNMt9Z86cqacgGUvVsjr7UbN7927uWesgzeVyekEcKzZv3lx6vLe5xXZfYGbVqlUVf0Zzc7ks8949u0qP9+zZi+MRcF9HT7vW/zTNfGz31fZhl5ioj3t19HUykPV1c0uu9Hjjxg3U1CTe5PI3whs3bop8vCS5r/v37x+tCGEL97AVAll+M1tFcOLEidS9e3c6/PDDpfuPHz9eX9AnjpaQhoYVJhHC2mZXKGb//ctLMmez9VVTVEY1TFGzAc2qEXpR0jLq6soTSLt2bUuP6xsacDwC7uvo2dT6X4pMxzaVWrLvUTqy415dfR1v7Po6kylfLHv26G4aDw0N5bTYzp27YJ4IaVwrESFsoR1+IR9WW58VZ7ESIUxsxEFweKkTYtfpGT4S0qNpCrT2V6V9xpfpFqojaikcj4D7OnrKMSHm37LvnE1Ff9yro6+TgVNfs7VljK+LabvRj5ekUOm4DqWXk3ww/dQJwYJpMQAL2NUQKRevIDC11uFvTGR1QvghguyY8PCkDlgsxN69e3UzDKtlzx6z/40wqwdbKZDtx9wxzz77LJ144olUM6voCim6mPyiQAhM5UY5Ys2qkNL5Jjk/UScEFOHrhMgqpvI3KxGpkIMOOojuvfderk0pmjVrVujtuOWWW+jII48M5bs8uWOmT58uBJCyWiA333wz9e7dW1+WuFgLhKXwsh/BBAqrh88W8PnsZz9LSUQciu4WsMty5n8I6mgQ3DFYy6dmU3RL21LJtcaCYOBHR9bgMnezgF0UrF27lrp06eJqX3bNZYLlzTffpCThSYSw4mRWBcr4YmR33HEHVQvCnbOPsu1IN48IVEytIWwsIZCdwGEpB6klpIKJgmXZBBXj2LNnT6p2cHuguGw75sCYLWCH41GF2FlCyqDuQ23DW0el7hiSW0JYfaxvf/vb+l+nTp30op2sEGdRqDAXyk9/+lP62te+Rh07dtQLejJeeuklfZXcxsZGPYOEeQt27ixnTq5fv57OOecc/XWW0vrggw+a22Rwx6xevVpfQG+//fajdu3a0THHHEOvvfaavrrvrbfeSm+99Zb+HvbHtjG2bt2ql8lgqwCz9rFyGWw/46q/rJxGhw4d6LLLLqM9e/ZQWECE+MiO8VS2XVG7gIeKqXDHVDlu1o5BPFDNI1hCvMWE/P73v9dLM7z++uv0i1/8gn7+85/Tb3/729Lrd999tx5y8J///EcXKMuWLaNRo0bppSoWL15Mjz76qC5KmJApcumll+opri+88IK+JMqvf/1rXZhYweIsR4wYQWvWrKG//OUvupC44YYbdHF94YUX0ne/+1067LDDdBcO+2PbijW32Oey2MxFixbR0UcfTaeffnqpntVjjz2mu3ImTZpECxcupAMOOEBvS1goK1ZW25YQuGOixipFF8ejGkmZBIfhFZ1CXBz9oMQxVxRoXbm2Y+VorNrpga1LNaRE1blu97DS429O7U5Ljne/dgyzZEyZMkW/wTzkkEPo7bff1p9fccUV+uvMusBEQBFmeWC1r6699lr9+cEHH0xTp07VRcT9999PK1eu1EXB66+/Tscee2wp5nLIkCGWP+2hhx6iDRs26MkezBLCGDRoUOn19u3b60KJd+Ew4cO+g4mQhoaGkmBi1hUmfJjVhgXCMusH+2PcdtttNG/evNCsIRAhTlS6dgyi8qOBO1RZbu0YmEKqEbu1YzhLCBRo7GACZM2GsC5rrRdhxuZP+UmhFX6mNsaEHH/88YKFm9W/uueee0rZocwtwsOsFMwCwrtYikt4rFixgj744ANdMAwbVhZGgwcP1gt8WsECTo866qiSAHEDawezoHTt2tVUCZxZaxjvv/8+ffOb3xReZ7+PWWjCACLEkwbxHpiKi1408N0Od0yVY+MehSUk3vR0fz31YAnJUSaTNXnp1m3YS/l9QmS/Dkw81AW2ii6Lz+BhF36WxMHiQIz07dtXFyFeaWxs9Pwe1g7mXmFLqBixEzxhAhGi2h0TfJOA10h4uMeqHDd1QiBC4sjCacGGJTJLQ1PTGr3kurFIZp8z/02r95ygP575PVbq/0DXMSEs+JPn1Vdf1V0srPKqDBZ38d577wnuEh5m9WB1txYtWlRyx/z3v//Vg0itOOKII/Q4FBbLIbOGsIwcY90u1o5169bpVhcWQCuDuYDY72OBtfzvCwsEpqoQIYKihjsm+joh3HYcj5rNjsnlEZlay/DnPn9jIhMheYMIYTEc119/vS4UHn74YbrvvvvommuusfyuG2+8kf71r3/pgajMjfLhhx/S008/XQpMZXElLHB1woQJugBgYoTFkdhZO1hWDIv3OO+88/SV7JcvX05//vOf6ZVXXtFfZyKDuXrY923cuFEvJnrGGWforhX2nueee44++ugjvV2sdhcLQmWw38FqfrGFZJmFhtX+evfddyksIEJUVExFnfDoQXZMDWEnLGEJAS7rhHCPjUOFWQlYHMXw4cPpqquu0i/cxVRcK6sFqxzOLuosTZfFcvz4xz/Wi3cWYRf9Xr166cGqX/rSl/TPYwu9WsEsHUxIsH3GjBlDQ4cO1VNri9YYlonDhM2pp56qp+MyscRCA2bPnk0nn3yyvlDsZz7zGbrooov0FYJZSi6DZdGwjB6WacNiVNhr/+///T8KC7hjFMSE8OlfmPaiAXVCaglrdwxiQoDrOiE2xcrq6ur0LBKW2WKEWRdkMDcLEw1WMKvGX//6V2Hb//zP/4htNrSDuZlYVosMlv0ie43V/mCZOezPih/+8If6H8/kyZMpDGAJcUA03yNFN4nHDSm6Vc6+q4fUHsLHhODg1zZe6oRgqIQGRIgD4mB0t3aMcNFDim68smMwuVQhqBMCPK4dk5WsHYOFLiMB7hgHhHnLhzsG/ph4BaHhcFQhqYzzAnYQIYCTpE4xIfyNpiy9FQQHLCE+yrY7gYtevBAtIbBMVS8OKbowg9U0/OF3igmBXg0PiBAnNO9rxwjJMbjoRb92DJfKj7mluuAXpZOdaXDHAGmwumNMCMZKWECEqE7RBZHAx+JkuUIhmFuqC8F3L4kJ4c9ZYwEqUGu4rxOCeSI8IEIcQIpuQuE6vi6LGJ1qhbduSC0hgokdB7+WEeuEwB0TFyBClK+iC6tIFGDtmNpAFBZO2TGhNAkk1RLCPYY7JjwgQlTUCYHuiAGoE1ILOFlCUDEVOBUwlK8dE0qTAESImuwYPiYEa8fEIDBVsITgeFQTgrCQ1QnhDjfWjqlthLT9lLe1Y4A6IEIcEK29qBOSSHcMZwnB8ahhSwjMYDUNf/Sdy7aH0yYAEeIR1AlJvDsmotaA6GNCkB1T4zisoiuufo6xEhYQIYoDU+1X+AShrB0D91jV4i07Jpw2gaTWCUHsWBRAhKiomAqzXszcMfwyulG0BkQVE4LAVFAGdULiCESIA4WCOWTaeQE7rjiWuqYB1xVT4Y6pVnizuXTtGN4SAhFS0wgL2PE3JlJ3TDhtAhAhHoE7JjnI3TG4w6kucjmnsu0ITAXuYkJQtj0aIEJ8xIQ4rx2Di1687nqQolutCMLCIUUXgam1jVgnBO6YuAAR4oB40YIlJIl3PdksYkJqN0WX2xfHvsZxXycEIiQ8IEJU1AnBNS9y+H6v40QIjkd1IVo3ZJYQBKYCd5YQXpjAdRceECGe8LGAHcZyRPCr6GItn5oITJUcWmHtGJyMtY2nmJCwGgUgQhTUCRGr8eGiF6sUXVBV8IvSOZ1piAmpbRATEk8wOyuOCcGddwxiQpCiW7UUPLhjcnkc/VqGn8uxdkx8gAhRvHYMhnK8LCEQhdUF3DHANXDHxBKIEE8gOyY5yC0hoLYsIfzpB3dMbSO6Y8yvI5YvGiBCFK8dg7Ecg7VjOEsI1o6p5oqpTsXKQmoUiCm8JcR86YMlJBogQlTEhAijGRe9OBUrgyqsLnjrhtQdg7LtwCXiYocYK2EBEeIFl2vH8MWxMJSjQm4JwfGoLoT165zWjsGFpaYp3VBqeRcJBWG1CkCEOCCa731YQhATEr0lhK+YiuNRtWvHyA4tL0wQE1LjlOZy+TjA6ufRABHihJ+1YxATEj1CTAjucKoVT6vo4tjXNJrDJICYkGiACPGkQXxkxyAmJFbLdmNuqbGYEO4xYkJqHXtLCERINECEOCDWlUB2THIo+n8LcI9VMY66AsuzAxNW7hisHRMFECGeQExI8tAQcFYrq+hy1VFL25CiC0yZjrCExAmIEAfEsQhLSBInHLEkAERhTdUJQYouKAEREkcgQry4Y1zHhFi8H4RHsd81DWX0qxinjBf+7Mtj7ZiappyiayVCOHcMl3QF1AIR4gm3IgQpoVHDJWYKqxrjDqeK64RI3DGICQG+UnTDaRGACFFTth3EAd4dgxidmogJkbyOFF1gBu6YOAER4oj37BihuiouetHHhAgLU+F4VBNCFgNSdEFggakYK7EUIU888QRdcskldNxxx9EDDzxguV+hUKB77rmHTjnlFDrzzDPpwQcfpFoKTBX2xUUvVpYQTC1VXCdEWqyMc8zhwgJcp+iG2JwaJ+tl527dutGVV15Jc+bMsd3vz3/+My1atIiefPJJ2rFjB02YMIEOPvhgGj58OCWBV/69hv7w9Ao64cj9aH2Oa/O+SczdZNa6zx6tC42+8gXbPdnQ/9Lp3ejf72+jNetb6LqvDaQDe7SnH9/3Nm3dUaCundI0+sTu9MicdZSTL3sQOOy6Pe7M7rTg35tpzYacq/ccM6Qt3fydYw1l0iNGC84d09ycp1t/uZAG9W1P4y84THjtv8s30ZTfvU/XfG0wDRnUzfd3sM+5+b539ODmn159BA3s14WSUj799vsXUvf92tAl5x5CP5qykJaubtZfa6xP0Q+u+Awd+9legX7nS2+somt+9hER9XVVrOyu36+nB2d/QqGjabR7925qbFwhbSTrnwtH9aSn/v4Jbdspj4jcv9v+1NDQQKvXrHb8uk7t0nT+6T3oxTc20o/+3xHUu2dH301/462PacaTy0uf89jf/ktvvL2Zbr3mGGrTkKHJD/yb2jZm6YoLD6P/nfIGvf/RXspmiC4a1ZPmvryBNmwVJ6wDumXpzu8eTS+8sop+/5e1JTkwYlhHuuGKo0yr265et51uv38xfeNLAxzHz89n/Ieef2WLZV+3kHjOGuF3f21JW2HeZvUOvzmuL5192kBSydx/rqC/zf+YfnLNMOrcsY3j/is/3kb/e+9btHtvgW7+1qF0+CHd6Z+vr6JHn11Jww/vTI89t54O7d+GfnrdsdSmwdPlPjRSmo/bg0mTJlHXrl11cSFj/PjxNHbsWBozZoz+nFlNPv74Y7r11lul+zc3N+t/PNlslurr6ymKybTNKZtJy0ouJK90I8ptocGDB9O7775r+zmZk5uJ0v7an8mtof0aPqYN+WMpaUw89zW6/frjfL2XWdBWrVpFffr0kS617YW2J31IezMHE+V30idPp6jHl9rq2zun/kObXjjK12de+b8v0fSXTtQfv3DXKjp5eJ/Sa11GLKbtqSOoXeFd2v5P+8nOjp6nv1E67r3qX6VVzx1PKgiyrxk33vUq3T27ta2DOrxESz9t7aciDfkPadeCgynI87TdKaspl20VIIz+7V6mpX/7vLDfkV+eT29vGkG1Su82r1DTnBN8vZe5rxpOXkeF7AHUp82/aPZ9B9PQK/bXXzvviPl0zKHt6X8fGaY/P7jDAvrw05Ncfe7A9i/Rsu3HEKXFi+x9ly+mb331CGFb35Gv0Jq9J+hzYvNLB1p+5p9mvU9fv3eIux+W30b5BZ1Mm78/+RX6+bPWfZXKbaF1f2mgbvu1ziVBs3nrbtr/3CxRuo6GH/AivfLwKY7v+czZL9GyHa3nWid6iz6eexi1O2MXUUYUnl8/YQHNuMPd8QliDvEypyiRRsuXL9ctH0UGDRpEL730kuX+M2fOpGnTpgnbmIgZN24chc0nG3eRlrUazJqexjV58mRqamqy/ZzOqfW0lfyJiHz2QNrQ3IEoQ4lj/sItjn3jBBvYlVKW1hqtWbOGna76s0Je892+ogBhXDv5PXrq5+W7ViZAGDvTh1X0+zftOZCorvXx+p09Ku7LMPqacc9fPlOaTYwChMEE4YoVHxmsUpWdpznDeXr0wO2m/jpu8B56+2WqWVbvOcH3GGJCr5Dtrz9etedzdOPPniWi0frzWYtH0N8WLSuNVbcChLFs22eJMua7/DkL1tBZJ4nigAmQ4pxo9zueeYG95k6E7F/3LjU1mQXNKUe3oZ//bS9RukH6Pi3bhea88BqddExPUsHDf/uIKN0qmF9fe4qr47Zma9fSebct149mz3uLKNMqDHlef2+PsrlENof07986biITIcwc1q5du9Jz9njXrl2W+zPLCYs1iYMlpK7Np5avvfzyy7R/lzoaONDZJNc0+wCa+oeF9OlOe//JH+YSrWs5zvWhYXfaV527g1QyfXaWNhXEgfyt0/9F7Rvlquhfi3fTSytbVXt9Qxvq169f5HfnKVq675FG/fqV75ZT6bTv9vE01DdYfk5Fn59axz0Opq1hWEKINss357cRZVovLAce2Ifq64NR1um6bcLzP17/Pn3l3NYLJM8Dt/ejkXM/oDfe3kLRodH27dupY0d2dyqKsHtn7U/N2QGl56ncRvr+ectKz1etXkUPP/SQ8J4zzxxJRx55pPSbfjZroMmK63cM7dkrumH5OV1vqxs9qeXohnMW0e69ebrv+c/te2NWOj4a27azbWufPn0tRWzbxpWlx59pP4/OO6W91PXauUOWvvO1Y6lt4z71xMG+e9GBn9Cjs5mLr8xv/lZPW7VW62m3bvsrOye77rfd1B5HUv/lHqepZ88e0t3q6uoDb3dQc4gSEdLY2Eg7d+4sPWeP27a1NmExsRGF4JBh15m9ex9IfXuZzXgyOnZoQ/97lXMMzPz/vEDr2I26Ef5E5ejU5lOafMO+k1kRf315Pm0yzNk3f/so6t5VnISK3HH/QnppZTn7pNKLGnt/pZ/BR8LX1/F9WUn7+GXjjZ9Tfq2ytovxK8EIBLV9bfv52i4qUKdSsF9Q38UHDu6fWUhfPc/6XLtg9GC6wKxPQoNN1uwulF0EjL///mfeJN4RnaFdNPmGsktgwYIcPXzf08J7xp52Nl1+udxt8PNZTWSM4PLb56aCXX6MWFqL/nu2bt9D9z1f3JiVj4+CrK0FV+cDn4U4dEAL3fG943397qMPP0D/43lq/j9p675700IA85sVKV8uDXG+MH5GkSDmZVVziJJWDRgwgJYuLd6JEi1btkzflgTsFi4KypQsfKbVR1qIEFd3HxWSlhR94leiNZLJxLnSYLKyYzTulOQfJ5UU5QXzflDkcvZrxiQFY9tTwkVXfiGyn/CD64tcPoDjta9UgTB/pOuk48MpI8WuPXnuzZmA52n+GOWD6JMA0TR+LFiPizhn+3ia5XK5HO3du1dX9vl8Xn/M/jcyevRo+uMf/0hbtmzRzTWzZs2is846i5JA3maiFBemUy1C0q4FQtDIfqatCOF+RHzO0VQpbVMQj7FPmU5qtd2UswgJcHA0c2listTcpGBuu1aRCDGKmEowikbz/Zmb8blPhFhkzAkixOFKaSdi+ZufoO8V+c9rCVBIG9F8pJALdahSactlDPKF+M4lntwx06dPFwJIZ8yYQTfffDP17t2brr76alqwYIG+/YILLtDFx/nnn091dXX09a9/PTHpuc0thXAtIR5vdsMQITK9YefL5yeYuChufp2IZK0dw1tCEhiZbCDNOQeaW4LLL+fXgUmyJUQ/nzVvlpBMxm5cBNcXwRwvzfYmhh8fTuUH7NqT48ZD0F4H/vPitv6QJty0WI+LOJfI8SRCWEquVVpuUYAUT5zvfve7+l/SsLtbUyFCbAwMUsKYcNPpanDHJLNsuyZYwOLdVncFoNRYQvi74jCEeViWEONzmeAIzRJiOF5mC6lzv6e0PHejUrAdH5W4Y/h5x1aj+YAfX7zYCZqUL0u7aAnh18niYbEscSX5TueAsfP5KXHHeLaEkHJkP9Ounbw7Ji6WkDJMhPDP4g5vtUn+6clfFO1cnZWcp2HESYUWE5KqLCYkSNdUMPEP3GfsEyRW48PpBsbWHcP97OBjQuSxJ3FAE+YIa3dMnC0hyZ/lAsZO6YYamBqlJcTYJq1gO/HxlpC4DHZh7RhBhcT9ipVUSwi5sIQEGTSpVYclJBVwTIhBxAQV/OsfzRSkamkJKVRiCalNdwwZLCHNFkINlpAEEbY7Jo6WEHOb7GcH0R0Tl8Eub0f8FxR0F+2eFPiLYpDuGNESErcLQ3DumKRnx4juoYLt+HAyMthZZvJKA1NjnB1D4lhosYhpjMvNoYzkz3IBY6d04+COicoSYkc2xtkx5YUEY9Mwe1IZxwypeCI/N9KKRAh/x1dV7piKs2PUuWMqyY4xPzaPD2d3jOYuJiRgFSJmx6ibfzVfSiFlW2AufjeHZpI0y4VCi00EtoqF2byeL4prV+mYY1Dtw9b5fomL4i5aPMqTspY4S0g1xIRkuIuM1V1apTcLSXbHpH3EhNhlxwQpQuwyBYNyx/Djw0mj8mnZtpYQle6YWMeEEO3ZK+8juGMSRNgxIWzFydi7Y5wsIbw7JjaD3WAJKVlE1LeP1dHxjWD9SNLpGXJ2DPdZSRYhRitOxdkxAcaE5IPIjhGsH/mKYkLsrNRidkwqkTEhKV8mPXEs7LVyx1B8SdIsV6XZMd4+M4wJ19wkDzEhsRvtRfFRtISop6KAPt4dUwWnZ4ZL9w5yAhctIVRFlpD4uGOCCCQWRYhmOz4cU3RdZ8dQoPDjS6UlRPNVrEz8sc3Necuy7XEl+bNczWXHkHLMc5z9yZHJxM8dY7aEFCewlHLLh987fpMFJVExIRRqTEjVBKYGHRMSYF8EE4TJFZWTZscEE5gaVkyISktI3tdnp1y50OJjoTaT/FkuTEuIgt7ynB0TwhEznsNOBZDEOiGpWMeEhGH58LtGitmCkvzTU5UlREzRpcRibHp1W0IK9pYQxxRdG3eMpu5mMayYkLyv/jZYQpAdU+2WkOC7KxNHd0wlMSGxSUKRx4SoCEw13uH7LXdtel9VZMdoyiumJqqbnNwxMbaE+MuO4d+k2f7+oCwhgQemcj8zyEUYjbT4ECFihWWiPc2whCQelQsUJcUSYvap2vdJXV0m/r7H0gwafPuM4sHvxdb0PouVlJNENqMmO6ZaipUZl0hwYwkJLTsmgLmQt4TILKr8+HC6gbFrT16hO4bv7rhbQvYiJiT5WA4ERXUmeCtCXLNjUokMTLXIjlGA8e7ItwiRTLIVZdqEiubCHRNgTAhfITO+86v37BhDdku02TFGgeQnO4bfRyJChMDUlP/sGM3/nOotJoSUkS8E4Y6Rf0acSxNAhLgeCGouYtVgCeEXt4uP4g6vTohRdPhdI0UmXoIpnR0RWl64cOVUZcckeBZLkjvGD7woSlUcExKROybOMSEpQ8VUi7knPvOymQSfvmqwPvFUiZA4WkJMJVM9WELiMdj5tWOE/xW0zzg5+r3YSkVIfErQeoetOcSXvA5wAufP0yS7Y4zWBeOpl/TAVKeYkExGcx086dYSEnh2TFq+Rk0cLSEtVpaQmMzLMiBCXLtj1Aw+rzntoYgQj9kxgiWE4oJcSKmwhBgtH36Fg8yCojIQTj0FcQVSuGOU1wkJsi+CyGbiRZHcEkIe3DHW44efngMvVhZSnZBCEO4YC8sp3DEJIheyO8Zzdkw6jnVC4ryAnXpZZLxjtCv9b4csvS45lhDZcWeWEFKSosuv4ZHk7BiniqmRumMMc6Gf7Bjx95jbls14KFZmWzE1FYolRKV7tMXPZxsGP+qEVAHWd55qLgZeRUXQ1QBlGAO7nCwh9bHMjrGqExJGdkxw7hi/6b5xdMcEmXlWLRVT+ZiIuGXHBHG8nGJC+GUrnBewc7mKblrdfBgnS0iBdZhQYRmBqVWB9d2aFo/smHS8s2PiM9iNQkqhOyYfkDsm0TEhsvMjr2zdDf5iEIYwV4UpBsQgQrxmxwQZH+N8vLxlx8jmEe7+xXQDY65EbP19vJWmLqvOHaMyWS3vWYS4t6bE5+bQTIJP32qJCYlfYKq5TZrrmJDYpOiW7NzFgFR1lhDj5Oj3YiubZP1m2sQDTSz0FGhgarWm6FJss2P8TIFie2TumPJj4/AwXmTtLtK8u0GlOyZO2TE5yQ2KpQiJzc2hGYgQA0jR9W4JiWOKbrlse7Ht6i7m5uyYAFN0Fa5VoRq2VghfjEuVJSTZKbrG55XGhFBgtWaMc6GfYS1aQiQihKvHZ5w7TKnvrsu2U6AIy1IovCcoePxsmXsqZ+G9jcu8LCPBp291uGO8RnKHI0Lkrgw3LqX4KO4Qs2MCsoTI3pccd4yb7BhYQoKomOrVHeM3mNJ4vPwETvK/R1ZIzc4SYrzIus6OCXiSDM0SUlBnCVFhAQ4KiJCILSHxdMcYNjiYeDNZ3h0Tr8FuFlDq64T4zY6RvS85IiTl6I4JcgLn+yXJlpAw3DH+Y5SMIsT0bQFkx3CvGuYOY1C2a3dMRmF2jELLZM6rO0ZiCTEfo1YKWnxPkvi2LCKslaQqd4y3E4Y/aVVRTdkxZRSm1hnS4vxnx5jfF+R6K2HDxo2Y3hhkdgxVhQgxCv50pdkxktPPb4aVcUE1qwuca0uI5Bzkg0iNNzAmcW9jieFdGUGLkLAW6Cx4/GzZcbU+RnGZl80k+PRVg0pzWzB1QlIxdMfwxcriMtj3tWPfJFj+DeqzY/I1mR1DodYJqZ5iZcFaQuTumGAsIVbxBpXEhNRxMSEmEWJyx9hlx/CBqaRsPoy7OyZnFRMS40t9fFsWEda52opSdLPxd8d4EiE1snYM74M2WjCCtIQEeeFWiya1hPBjKcgJnD9PbQwDsceoJzKmCqop/a8iEeJXFBuOl/kCp3mrEyKJCamv4+LJjDEhRnFv644pP85y7uEgEKq6Kk3RJW/7exIhcZmXzUCEuJz0gywCFPeKqcY2Oa3MyZ/08RnsVu6YgESInSXE58W2+iwhWih1QqrLEmLuI6Po8Fq2PaiYED+WEL49Tu4Y4w2M2cLozhKSDXhAhJcdo3naXxZwbHmMYnNzaAYixIDlBURVnRCP/sugc+CDKNseS0tIqZyxmsBUrWBXJ6RQg5YQi5gQRYGpVbOKrikmhCoSITIR47fWTCApukJ2jIMIMZybxous+8DUZGbHFLym6EoOiNUxis/NoZkEn75qsD7RasgSYgxMdciO4V1KWmyisNW6Y/i5yDgx+VoDwqJMduKzYxRN4LxgC0OYh2YJkZw+lVtCtEDuzFtyqYqyY+SWEOtCh82G23pbSwiFUyeED4gOmpzH4yS72TEfo1Y0iq/PMi5XjNhQiHmdEK9l3v1g/I60w2/XJ8V9poH4KG6r4NqgREhKQWBqlVtC8mruGhOsQUzxLLKYDqPosM2OSatzx+R9LE7pZAmpt3XHuLeECO4YldkxCk/HgmdLiGS+sDhG8ZmXzUCEuFajtRwT4ua3x1WEiJYQFROG0fLhu1iZZIbzW3MkHoiWkCCtOtVbMbVSS0hwY8gccO39M/iYWpklpL4+7To7xq7omrCKbkLdMXmPny2vK2S1d1zmZTMJPn2ro1iZ18WWwkjRNblj3Pz2oiUkNjEhRXeM6YXg3THGsu2BBqZWU3aMGhEYhnUwtJgQyYxstHyE5Y4xx4R4r73DiyJZgHtDnXVQu5eAb/69QY+HTEzrhOQ8WULie6mPb8siwmqiVJYd4zkwlcKvE+LBEhIfxZ0SJr5ywTUV7piALCHSSSUpIkR+zvDXS2tXp3eq1xJSYXZMzNwxogjRHFJ0jcXKNNfZI0KKbkZhdoxKd4xW+U2L9TGKy7xsJsGnb3XUCfHqjgkjCM/kjvFiCYnNkJLftWkqsmOMd4x+64RIxp7f+JI4wASgqjohfLckWoQEnB0jEzF+x6N5Fdt04BVT64OyhCjNjokmRbfg8GVyS4j8t8dnXjYT35bVytoxHk+YOGbHtFJ0x6RrIkVXdMcYfOc+UyJl70tOYKpVdoyaapP5KsmOMRUGVJIdE1SKro/smJR92wQRYlw7xkvFVK4tQZdtF7JjFIqQnCGew0nweLOExGReTlTLIkKl0k1KdkydMTsmkYGprfBVCIKEnzC9RPF7X0U3KSLEwh2TUnNuVYs7xiig3FhCvGbH+A+UrnwRNGd3jJ0lxGgZIJdl2wPOjsmGZAnRvIlH2dxgdYxgCUkQYU/6sXTH+LCEBB1zUQmCGVPR2jF5btLzssaFZxES4KJvUceEBHluCQuWJdgSErw7hgLLjjGLaz/uGO6x5EagoYFf/DJtexG2Gz98jJbSmJAQA1Obm/Oe6wpZH6P4niMQIQasB5mag8gX64ltim7CYkIEDWJ6NaCYEJtiZb4tIZL3+c20iUV2TKogXBCCXTumOrNjMhVmx8jeH1h2jCnzzflz+bVwZBZVITumkjoh3Hldx63qHXh2TIh1QnIOlhCpO8YyOzH6eZkS17KIsB7oqXhkx4Qw4VYUExIDxS0GeBnrhCQsOybp7hhFi39VrTtGQUyI7+J5AbhjnMu2Wxc6NLY7KndMaJYQTZ07RuqniwnxbVlEWA0yVRdXr3dx0bhj3Lwr2It8JfAnb8pgzdECOhnDsoQkWoSkNENQH9wxyt0xAVpCTNkavmJC7OeR1rlGLkJyPi0hgQemRmUJyTlZQhATUpWEbwnx6o4JP0U37SEmJA5rFAiTZ6ntwYoku8BUv5O+bM2Z5NQJSYVsCSk/hiXEwRLicwwZj1ehkPGeHZO2n0f0+c+i0KEp1sqlJYRf1TtJ2TF5r+4YyXE1H6Mi8T1J4tuyGokJ8aravVZYDSJOxUt2TBwsIbwIUdUawR0TUJ2QarSEKEvR5T4ryZYQ09ox6cqyY2Tv9++OMVhCKPjsmNZjJ587vBQrEyumBntZ4+fcMC0heR8xIdbHKL6X+vi2LCLCrg3l9YSJpGy7J0tI9EOqwPtK9llAVC5g58VsbPuZherLjhGKlSlawC7RIsRHim7Kxj8qEyF+V3U2Hq+ClgncHaO7oy2C2o3nkZ2Fkc+sCXqO5OdDvkhh0BQMP6+5xf7LZMfV8hghJiQ5WKvteGTHhBMTYpz0khUTYm8JUZAdYxIhVINl20POjtHkdRwSnx0juYbwlg8mQOxESEahJUTzI0LS8kwZMZMlL03R9VQnpHheazkKmqjKtud8WEKsj1F8L/XxbVlEWB/3mASmZuIeE5KOlwgp1gkpLZ6lICbEaLaGO0bqjgkyJoT/rDCsg3GxhNjFg7S+TsrKthvPbTfB+o7uGH0+04LLjlFgquBvyowr/ap1x2ie5wvL+ReWkORgOdDdmQOUB6bGPzsmnu6YMgGJEO5zzAFl/iZ9+VoQyQ5MzYQQmJpkd4xRQDnFhDiKEElX+K01Y3xbgbLCczdB6Hx7ZG1j7mirQodGd4P9uVB8rwoREk2xspyf7BjDMSqRstgeA6K/YsQMq3Gu6g4/jpYQY5vcWUJiJEKk7hgtUDHJW0IKCsu2J0eEmGEXHVWmbCEmJPqELN8Y70Fkgio6S4hmKzrciBD+BtzSEuIyJsTeHZNWZwmJyB2Td3THyCwh1sfEaUG8qPAsj7Zs2UK33HILLVq0iLp3704TJ06k4cOHm/Zj+8ydO5ey2davOOCAA+ixxx6juBP+2jEOF+1CM1G63nJdl7hkx6SKft0YmP1kKbrBB6ba1Qnx+ZmS9wUZzBmNO6b8HHVC3Ah+8z688LDLjNFfl3xAUGvHuBIhhvnKjSXEqk6IKSbE5meU31tQOh8qdcdo9tlB7twx1uMjl9OovnxokitCJk+eTF27dqV58+bRa6+9Rj/4wQ/oySefpE6dOpn2veyyy+jyyy+nJBF6TIhTYKoeaFUfcsVUowhxfk/QF/mg3DHqAlPTvlIJPZdtDztdK0B0dwwfmBpkdoxWHSIkE4Y7xmeGlVmEGN0xWcf5SsiOkZakL8eEUCDZMWrdMUqzYwrOa8OIr8tEiPUlnc0l9TGo41SRCNm1axe9+OKL9PTTT1ObNm1oxIgRNHDgQJo/fz6de+65vhvR3Nys/wkNy2apPgLZZlesTIU5y9nKIEZ7M2+CarOacSJjd7SO31n6HWnf7Su+r9LflxfWxC62vSySgug/jfsc00JbuYKv75AJDr+fFVZfl5C4udi4SRnEWVDfx5+nYZwTqvra6KJg555xP6MIsfut8hRdf2PIHGBtvIBlHOcrNr8Vv1u2BhX7vSwmRNvnUuHbmTOsbc9esv4dxZHW+h1BjgdhJWhN3VgrGLqnpSVn+13G/mnFWmQ0N+eoDbdgoMpx7SSWfYuQlStXUtu2balHjx6lbYMGDaLly5dL93/44Yf1v379+tFVV11Fw4YNk+43c+ZMmjZtmrBt7NixNG7cOAqbXbv2WLySoqampsC/b/367US0v+XrKS0nnLrbtm5W0g6ejRvXE1G/0vNcrsX5O0u3COmK27dq1aqK3r963Q4iarXMFQp5vT1aqX2VHMd+gqm4+Dnbtn0q7LX90x2+vsP4OYwtW7cpPd6V9nURjTqbthUKOdq1c1fp+c5duwP7Lbt37y09/vTTrcrPCVV9vWMHO//L7Nmz0/Rbcjnxwm73W/fs3mnatnGTvzlj525xLtRSGdvnsvmqpWVv6bvzefFGk7Fh/ToirXvr52niublx42axPTt3Wf6OoisjpeUDHdeMzZs2sCtd6UKuaqzt3dsiPF/z8Tqy+6pNm7eYtsmOSZEVHzVR544NFDSyvu7fv78aEbJ7925q166dsI0937Ztm2nfiy66iK6//npqbGzUXTfs8SOPPKLHhhgZP348XXLJJbGwhNQ3rLB4JaWLqaAppLc67CGq3f3376akHTyr1osDuaG+zvE706klrQ9SGd/tY4qaDeg+ffp4UtJGcrRFcC2x9qTT7+1rX+tz720Tb1NS6fLvbNtWPAnbtGnn6zsaG80nc/v2HdWMu4D6ugwTfmZfepcuZXFS39AY2G+pqyufp/t320/5OaGqr7t0+UR43qGDeeywOZSPCbH7rR06mK9aHTt29tU/9fWGuTBVZ/9cMl81NjaUvrtN/Uemvfv27U2p1B7pudm+I7v4l2mwGz+p4u9uvdkIblwTHfBR+TelM85zoV8y2ZXC827d9nc41uxmkVwck1Z69OxFPfdvT3GbQzyJEHYy7NwpKm32nFlHjAwePLj0ePTo0TR79mx69dVX6fzzzzfty8RGFIJDhnH9ghKpdGCDmsdp2WkW8KkZ9lfRDrs2sa9z/s6yO6bS9rH3V/YZKeER+yw+ZsXPZxtNjmycFD/HGFCn+f0OSbwK0z4qj3flfV36JPOWlBjzxPdZpfB9lc2qPydU9bUxCJwFajqtFWP3W2UxYyxEyk//mOfCrOPlwzhfsZiX4nfLmlBfxz5Dfm4Kmfb7rB3Wv0N0xwQ3rtl8mK343HaD5un3WtVpsb6kq5pLKu1rT+/s27evHheyfn1ZgS1btowGDBjg+F5W5U8z9nIMCXvtGKey7SmDj9VrhVU/1NUZ1qpIOx+3dLEYWByyY/jAVONh89k+Y7yGWCdEXdn2GIc6OParcRXdIH8LHyBYVdkxDoGpdtVSLbNjAqoTQuk6++eS+Yr/PfLsmJRloUNz2XjrthbfW645krzsGM3w2X5SdGXHpLR/TJeA8DQjM4sHC0Z94IEHaM+ePbRgwQJaunSpvs3I3//+d919w/yZzz33HL355pvSVN64YT3Qo8mOKaa+hjnhGoWRu68s1uGIPvpavJgXH1cmgI0ZBkKdENNqowGm6CamTohMhKhbBl3IjgkhY0wVxvPZqU6IE9I6IQGtousG43zlmB2jz3/yzDovdUKMlhBVQlELtWy7Zru/1+Pqt4iiajzfFrK6IBs2bKDTTz+dpkyZQpMmTdLTc5999lkhkPShhx6iUaNG6fs9+OCDdPfdd1Pv3r0p7sRtAbu0UYREUKzMjfGAj/KPOlNBlqIrZmkUArWEBLWAnTxFN54ThwnJIGGZEaqWQefvSBMtQjxaQpysyTIRw+pD+MHP8TLOV86WEN5VmrG1DNq1p1gfQ4UlhC9ZYOmuDwDNWCfEwXLhdW6Ia7q/5zohXbp0oalTp5q2s7gP9ldk+vTplESsz3E1g6/eKSYkla+owmr4dUKiL4qjSdeO4UWSfLL3ZglJh2IJSYw7RhoToonLoCtaOyYMF6UqjKJBdn47FShzen9g7hgXGOcrfiqR3W+x+c+q0KE/S0jwJwzfp2qLlZE3S0iViJDknr2KKBSsAlOjcceYLSHpWLpj+It8c0u0ZT6FgqmlR7wI8T67Gn+TfUwI1dTaMbplSWoJUVi2vVqKlWXCcMf4a1shdEuI0RKgxSImJLQF7DR7d5Rpf48/taUFIiQRWB93VWvHOFVAFE/qMO766k2Bqc7vSfOWkIgVt1i2Xfiv9XUfjl07d4zZEqLVlCXEytyvB6YqiwnhsmMS7I4x3oSoCEz1u6qzn+NlnK/49sh+m/77uUKHtpYQLZqYEH4+VBkTopkCU4OOCYnnZAIRYsBykCnK+nC6ySkvQR+iO8bFxGhnCfFbJlrNAnbFgNnKLCHmSHVrEeL7zlPSrCRYQqwmN90SoqjktZAdk2AREufAVD8XXON85W4VXbkI8ebmLFpCtMTGhBS8Zsd4PJ/8riGkGogQA2HP+c5rQRSS4Y6JkyVEkqJrLB9e6d2+EBNizO/3bf5OZoqulehkuj0Ud0wI50RS3DGBpuhauaY9zFeCO0ZqCSmn6Bpv9DxZQlLq3DH8fCivzREMmuH3ObtjYAmpSvI+TjyVpCOwhJhEiFdLSMSDnQ9M5bZWJkI8ZMf4vdjKui0J2THWlhDmjkkrObf4O9JqiglJS87virNjfIZo+cqO8ShCWn+bVXaMe0FerjGiIEWXC64OMzumxSGryevcAEtIQohbPbVMFJaQCt0xURfFEUJCitkxwuuVu2P4wkq1nh1j547hs2OCPLf4Y2wsrpckskFnx3D9XWmMkp/jZZyv+N9jmrr0FXetCx2aLSF2AmCfJcTw/cEHppIyNPIaE+Lt86O+ObQiuWevIlRGPyfWEuLRHdMccRQ2f7KVml5hzIr5N1m7Gfye6/mEihCrbCgmXlVN4GJganKnseCzY4JM0VXsjikF9sgLHZosjBHFhAjuGM4NG312jObp85EdkxBiZwlJFzytNaOidombGzFmeo+zOyaxlpCYjUcZVpYvY7GyIAV+tRQrM2a7yc61irNjAgyU9jpfCdkxpqYVbAsdGtutRRQTws+HamNCUp4sIV6Pq1Oga1RAhMTcEmJctyUUS4jBpOu1TkjUg13mjqk8JkRzbQnxKxySmx2jhZ8dw30lLCH850WbHWOcr9xYQoyFDitxx6iJCQkrO4Y8HbeaLdte7cTNEsJbGMKKCTGv2un8Hv4GLerBLqboiv8bX3cLYkKssbJ8sZtTsdokqQlMTbAlxHg+q8iO8W8JSVU+X3HtMbfNbAnhx5LpvHJjCTF8f6KyY0ixJSSmNzQQIQGkpanEaN6M4q7PTfZBnNwxgrulpEK0CouVGVJ0uVPH+HODtYRQwuuEqKk2WTUxIcY6IUqyY4Irnud1vrKvmGq2hPDxRZ7EfSqrLiYkG9HaMflg146J2kJtRXLP3hpxx2QzmmP0u2q8u2O02MSEFMURPzmpLlZWa5YQq+PNtIGqZdD5a3GS144xuldlyzh4y45JBzaG/FxwTfMVnx1j+BnF+A3hBoaLLzLeuVu1h48jUZEdE50lhAKOCYElJBHE7TAZfaxRFGZy5Y7hHrfEcO0Y3l0kryNiT7Mp+DIdTnZM3Aakl+yYlLpl0AVLSIJFiBt3jLfPCzI7JoD5ys4dI4sJ4U4C40XYqj18HIkKSwg//6m8SdUMmTeO2TEef2pLxKUTrEju2asIleY2PxjvLJxW3VXTBhfuGG7yiVNMSFBrxxjvImxjQgJ0xyTZEmJO0VVUrCzBMSHGGidOvyXM7JggLCH2xcocYkI0d+0RUvIVxITo7jCtqIjCc8cUAk7RhSUkIcTNHWM0fGRjagnhTar5OJVtL90Z8e4YCtYdY5osvX++VbsKCY8JUWUJ4U3XSY4JyYZgCQkzMNV4KOwXsNNsCx26LdvOu3BUWEJ4q43KOiGa0R3jmB3j7fNzMTWrJvfsrZHsGHNMSDxFSJyyY3h3S2ntGK59waTo2q0dkwrOEhKz8ViJJSRIKyP/WVWVoluhVUdqCfE5hrQA5is7d0wxJkR0x1jfLERlCdnXmiqoE6JRHEnu2asIWEIqz46J3hJSfszdhwfrjuGqOwbljpE1K6YB7e4sIaYU3SCzY8KtnROaJcThtzhmx2SCzI6p3BLCr4XjxhIiZMcYxb3F+OHfo8wSEoYIIWN2TLCWEIiQhBC3mJC61syzErCEBBAT4ic7xvQePjsmFZA7JjhTemyyY7gYpkBjQrj+D6OKsCqMokGW6eMUB+L0/jBjQkzzFff7jGIxRXnbGxizuHe2hBjrlARGqdJeeCKk4HDcUCekSlGpdP2QzSTPEhJ1FDZ/shXvjPh53I+lpsVTdkyqprJjrLKhjJaQQGNCON98krNj3MSEOFk/HGNCfLtjgo0JMf82zfYGxng+WHUDH0ei3BKiMiZEU5sd42fNrDBI7tmrCFhC/GbHxMfsJ1RMTcncMd4/0/SbuBU/jZNjkO6YJIgQ+7LtiAnxkh3jdK7Ffe0Y49fztUGMFlVpnRAbS4h1TAh/vlePO6YQtCUE7phkEDcRYrSERIHn7JiIr5z8nWOpTgj3ejDFyqwtIb7dMZKxF+R6K6qwOt6tlhA1hZ6qNjumwvgWmYjxL4pTFc8V7iwhvDvGJjCVogtMTRWzY5ReMlNKLSFRz8tWJPfsrVF3TBS4c8dQfAJT+a+XqJBgYkKs61/4jX1IbnZMwXLc8BV+lVlCqskdU6GgCjJF189caCdCzJaQfSKE22ZXJ8QyRTcfnjtGbZ2QtPDcaRqFJaRKiZslpC6CMu2VBqZGPdgFS0gpMDXY7BjBHeNlyXHP7pjoj7/vFF3T2jHBfWf1WEJSngS/c3ZMtIGpZneMc4quGJjqPUWXf4+HGF6PaMotIZrJHQNLSE1SgCWkYktI1EVx+K8vrR1TYdl2swjJlNasyAdmCUlmdoxlTIjRHRNgUJ9oCYnXOeuFjMGKU2m6sexczYe5im6F7piWClN0VWXHpEKwhJDqFF2IkGQQN0tIfV0qGSKEL9secRS2EJha/F9YRdf7Z7ZIJoSiQDAFpga5gF085w0Bq+PNLqj8MgNKYkK0gqel7uOG0YpTabqxMdA16uwY3lVmFIvFxeb4w2ebHWPRHv4GIa08MFWlJSQtfmPA7hh+jZ04kdyzt0ZiQuIgQtzcacYpO0bujqk0MFWz9EUb79D8ClnZ+5LgjrGyfOmWEEXLoJetKgkwFdlgXAuq0rVjslG7Y4yWEKFYmdEdo9nXCTEFfEcYmBpHEaJ5+3xYQhJC3Cwh8YgJ8RqYGh93jPz1IAJTyxYAc4pucIGpiciOceuOUWQJSTJG0VBpfEv0dUJS3su2W9QJ0XxYQlTHhITpjsk7HDiv01hcqy9DhJiI/qLvVAEx9tkxhTiuHcNPbootIZ4/3c4SQgmumJpSZwkpnacxnVldYszsqTxFN0B3TNCBqSZPk8QSwjXWjyVEfUxIjCwhBW/Hp4DsmGSgsiKeH+pjYAkxTx7xdscIVdtl7hg/xco8WEL8XmyldUJiZpmLS52Q0hGFJcRRxGhRpujaZcdIgsZFd0wqdpaQMOuEFBwtut4+PeqbQyvidcWNAXGLCamLQUxI0iwhvLul1KwK64TI3lOMyg+qToisVcbMm6RZQviLUqBrx1RJTIgx3qrSBexkIsbvMgJ+bsiMIiRr644pWkLK21pydim68va0VEl2jGa4HDtlx2DtmCpFrdL1ToMk2j2e2THyiSQKxDujYkEkefCbW2S/qWgGNlorfFtCJBeLINdbiSI7Rs9c0YoXCbhjjBgze4yBql6RLmAXYkyIMcVYzI5JS7NjMulKA1PDyI7Z97nc6tnBk/YYmOrt+OTkSzxFTvRXuJgRN0tIQ330h8hNRUr+BixqxS3NjuEtIT6aJ7OEFCdMsyWEaio7xs4do1MseR1knZB9U1f5DrU6qDg7RnKu+i+eV7k7hhclxt8mX1xS8+6Oka4VVQXZMZr9/l7nGT8W4DCI/goXN2I26dfHwRLicQG7qAc7fwdRqhMivB5UdowWaEyIbJJNgiXEzh2jbvGv6ogJMRKn7BgKIjvGRUyIVaFD99kxXGAqZ1UJklQE2TEFh6HtPTsmnpNJ9Fe4mBE3S0gsREjCUnQFv7lEhQRStt3OHUMBpujGTBT7soQoECHlu0aIEKf3+49RqjwmhC8jb2UJ4YWDaAnxXrbdxVTli6LrSO0lM63UHRPX6svRX+FiRtxiQpJoCYlaccuzYypL0ZWdwMU7MOPFtdaKlVkd75J4Ld3SKrCEKFuwLBoqLdsuEyFhZsd4qhMizY7hzlPT2HeOCVFeJ4RbMyrwb0h5c8fAElKlQISYcXN3IWbHUKTwIqPYLr59QaXoNrcEXDGVEipCLALeTO4YxIR4XkvGe3ZMKrAMq0CyY1zEhAjuGC7I2WQJsZib+feoy44pfm54Kbr5fMB1QmJ6qkR/hYsd8Zr02zTEYAU7FwjrP0S9dozEElJpiq4sXa5kCTEGpvp2xyQzJoQvFiVPP4U7xi0VZ8dI3u9/DFWeHcO3x5i5UxQMVkHtZnFvIUK496haUDkVSnZMRmlgqlPKb1RAhMTcEhIHEeIUkW+cfKJW3EJ2TOl/3swbjDumeFIHlaIre18yYkLsy/2nitkxgZ5b+z67xmJC/GTHhBkTYhuYmrEITLUodBinYmWhxISkvMWEeJ0bop6XrYjXFTcWpKrqzigs4lWsjAJP0ZX9JsuYEAowOyZm49FLOWijOybYc6uU/0vVhIrAVN8pugFUTOXb46ZYGX+emdsdYWAqFxNSUHY1T3u0hHj7sVHPy1ZAhMTcEtJQnxARIphUKX5rx1ScomveBkuIQ2BqRl3J62qNCXFTkyes7JigU3SzHi0h5qyztOP4U5aiy8WaqLMopL0Fpnr8dFhCEkO8Jv0kipDI64RIMnT5w+rPHRONJSSmNy+esmPUlLyu1uyYCkVIkMXKAnDHCJYQgwiRBY3bxoTEwRJiEwNV+Zekhadwx9QomiE4KGriIELcXLT5ySf2FVN9nIyy9zTnAs6OkVpC4n+KWvWnuVhZkL+laAmpMhGSDX7tGP+WEB/ZMRm7mJC0RZ0QefCk+ae6yI5RFZjKWUKUBd6nMmpX0Y3pqRL/Ga7GLSGNbbKUBMSJJMqWyAPV+KOaDyw7puhmcGc2dkImOJIQE2IVdV+8K1dR8lrbN2GnKKYLYgS0lkwQIsZ/yrj3thhdLvV22TFpSXaMbWCqG3cMKYEXu8WFK4MkJxE2zu4Yb8cV2TGJIV5dEgcR4j07JkbumJTMpxtwsTKFdUKSHRNidJkE746pNktIpeeiTMTEJSbEVXaMEJhq/P4I3TGl7Bg17pic5DOdjpv3tWMolsTrihsHFFbES2qxMu8xIXFM0ZW/XtHaMaXJz91kWbXZMQ7umHJMSPDuGFK2amr1EGpMiMF7nOZEk9FVlHIodOjWwhiKJUSxOyYns4QEHRMS01MlGVe4Ws2O0fLCSRxn4hQToiJFV24JKWbHpANyx1S3JSTQc2vfzUKtWUL8oEVoCRFeMwWmmi0hfLq3a0uIpEJy0gJTc5LPdBKPXm9Qor45tMLzrLBlyxa65ppr6MQTT6QvfelL9Prrr0v327NnD91000108skn01lnnUVz5syhZBCjSV8rUErVWVUrlhBJ9/laO0aznvyCyo4pjb1CSwCfFUdLSPB1QqotRVcF/sdQ5dkxPCZLiGPFVHJnCeFCNFRZQsQFOkOyhGhOQcr8nVV5zkiaJcRzwMHkyZOpa9euNG/ePHrttdfoBz/4AT355JPUqVMnYb8HHniAtm7dSrNnz6YVK1bQ1VdfTYMHD6aDDjqIouLXf3qLnvnnZvudUidQfNB8XTCjzo558a0GGn3lC36+iHbv3k2NjSsqKn24dE3G1hJyz5820CNzvbXv30vbmrbdMWMd/eGZtbRXGyxsb9Y6+/r95c9hs2qd/mhXfj9/felEQH3NeGt5G3eWkHTb4H5L6iThs6sZ/vzz40rcsqebr37X0sM9v8do7eBXrLZM0eXmjifnF+j9j1rbur25n3CF0lLy8fPB6vJOFa7/58od8/UfvkntGoP9/L3N7N8RwramTfbnvtg/udKcYcV/lnWQft7PvnsYHX5Id0qECNm1axe9+OKL9PTTT1ObNm1oxIgRNHDgQJo/fz6de+65wr5MfDDB0r59exo6dKi+79y5c2nChAmmz21ubtb/hIZls1RfX09B8tjzm2j+ilPsd7JR0uoq5dmhRd4Gdo1y+l7+JF3XMpzmLKGYoOlt5xe2envTyfT2pso/ufQ5hrNIy+5Pc5aIE4orip+j5VuXPk2lKZ89kOYsOZCSCOtz1vcla0W60V+/yOACjqM5L91TbJ+bdsr2aWhoKD1u27at59/bku1Pc5b0J8/4sCqY9KxWPj5GI0nx2PFzR9Puz1HTEourU6ad4/gpfn/QY4J3xyxoCmgMO7AzfZj9PMr3j8YsIfbKaLN2tPTzvrpkCR16cLdAx7WXLC9PImTlypX6SdCjR4/StkGDBtHy5cuF/bZv306bNm3SX+P3W7x4sfRzZ86cSdOmTRO2jR07lsaNG0dBwlxEfvnqsXOoqWkIqeDio9+nh/89itK5T+jw7m/R4s1n6tu7pP9DhZae1Da/knZlhtKxPdS1wciYz7xHsz8YTdmWJjrt2HbU1NRku/9pw9rQ/fNaiNL2ajxsTjpC09t+3OC99OYGSgT7Zd6mXKGOtqeGUVJJ5TbTUYfU6X3fr/NH9OGOgUq+Z1C31dTU1I6SwKpVq6TbB7VbSkt3nk7dUq9QU1Mv0+uXX345/eEPf9Bv1O69917Hc7Fr6mPapEVg0c1vp+MPr6fjD5hDr64dRW1y71KbDJs7duovd+tQoGzLR5Sra7WGDxu0Q/8tpx6VoafebBXdlXLykSnbvvbL4X020ScrKJY05t6hzm220Npc0TpY5oRec+iVVSfpAs6KjRs3Oo4pO2R93b+/e9Gb0jzY9/7zn//Qj3/8Y3rmmWdK2371q1/Rtm3b6Ic//GFp27p16+jss8+mN954o5RS9tRTT9E//vEPuu+++yKzhMx+cTktWOh8JerVvQ19eeQAevG11XTCUT1p7ovv0ZWXnFBxDr8dTzz7AR07tAf16dWR/u+ht2nrp8109deOoPbt6mnz1t30txdW0CVfHCKYLlXC0lgf/dt/acTwA6lXjw6u3rPo7bX0xNyVFXyrpgvYjh07BhI/cMpx3WnkSeWT4cm5H9Abb2+p6DMvHHMQLV+1zfQ5Hdtn6bKxg2n640to+w5mGvUH+5zv/M9Qvf+n/vFt2rlLVR2MYPuacd4ZfWjD5t30ypsb6Rtf/gwN7NdF397cnKdfP/g2fbJpLwVJ7x5taMLFQysuda4adqfIJuo+ffpI5xDWPw/+ZQldeNZnqG2jXMSvXbuWduzYQQcffLDj9+3Y2UyPP/shjR7Rj37zyPu0e69/q0CPrg100dmDSp8z9DMd6ahD96c/zFqmvz765F60e2+O/vnGevrquQNpyKBu+th98On36axT+9N+ncW783UbdtD9D71Hgwd0pIvPKbsx//n6Knr2nx+bvr+xIU1XXjSEHn7mQ1q/WbxGGDnjhJ506gl9bPvaL+w3/e7P79KH+wSVKgb0bktnntiXpj2+RIh1sYL1z7f/53B93Ez9/WLasr2F+vRspHPPOIgWvPExXXjWIbRk2Ub649OioYDnmxcNpn69xXCKSse1l773JEKWLFlC3/rWt3QxUeSuu+7SxcK1115b2sYmt9NOO0133TB3DONPf/qTbglh+ycJ1tH63Vy/fkpFCEBfhwn6OjzQ1+GBvk5eX3t6Z9++ffW4kPXr15e2LVu2jAYMGCDsx+6uWPDq0qVLhf1Y/AgAAAAAgGcRwuJBWIApy3xh8RULFizQhQbbZmTMmDE0Y8YM2rlzJ73zzjt68OrIkSPR6wAAAADQ8WxDmThxIm3YsIFOP/10mjJlCk2aNElPz3322WeFQFKWBcMsIqNGjaIbb7yRbrjhhkjTcwEAAAAQLzzFhNQi8DGGB/o6PNDX4YG+Dg/0dZXHhAAAAAAABAVECAAAAAAiASIEAAAAAJEAEQIAAACASIAIAQAAAEAkQIQAAAAAIBIgQgAAAAAQCRAhAAAAAIgEiBAAAAAARAJECAAAAAAiAWXbAQAAABAJsIQAAAAAIBIgQgAAAAAQCRAhAAAAAIgEiBAAAAAARAJECAAAAAAiASIEAAAAAJEAEQIAAACASIAIAQAAAEAkQIQAAAAAIBIgQgAAAAAQCRAhBrZs2ULXXHMNnXjiifSlL32JXn/9dX37M888Q8cddxyddNJJpb9169ZF3dyq6+e9e/fSXXfdRSNHjqTTTz+dfvWrX0Xd1MTzxBNP0CWXXKKP3wceeKC0/e2336aLLrqITjnlFPrCF75AN998M+3atSvStlZrX8+YMUOYOz73uc/RySefHGlbk0xzczPdeuutdNZZZ9GIESPo0ksvpcWLF+uvLV++nK666io69dRT6Zxzzom6qVXd188EcF3MKmp3Ypk8eTJ17dqV5s2bR6+99hr94Ac/oCeffFJ/bdiwYfTrX/866iZWdT8/8sgjtHTpUnr88ccpl8vRtddeSwceeCCdd955UTc5sXTr1o2uvPJKmjNnjrC9T58+NHXqVOrevTvt2bOHbr/9dpo2bZouDkGwff2Nb3xD/ytyxx136IIb+COfz1OvXr1o+vTp+vh9/vnn6brrrtMvitlsls4880waPXq0IARB8H0dxHURlhAOdhf44osv0oQJE6hNmza66hs4cCDNnz8/6qbVTD+/9NJL+p1kx44dab/99qOLL764NNiBP5ilg/Vxhw4dhO2dO3fWJxUGW8cynU7T6tWrI2pldfc1T0tLiy6+x4wZE2rbqonGxka64oorqGfPnvq4ZZbTuro6ampqor59+9IXv/hF6tevX9TNrPq+DgKIEI6VK1dS27ZtqUePHqVtgwYN0s17RfM1cxGMHTtWN7sCNf3ML+zMHhe3g+BhplN24WRm1H/84x80bty4qJtU9TChzcT3McccE3VTqmpO2b59u27dA+H2daXXRbhjOHbv3k3t2rUTtrHn27Zto6OPPpoeffRRXQ2+99579L3vfY+6dOmidz4Irp9POOEEevDBB+nII4/U3THMPcP2B2pg45lZpTZv3kxPPfWU/hyoZfbs2TRq1Cj9rhJUDnMl3nTTTXqsQvv27aNuTk319dEBXBdxFhjMTjt37hS2sefsrp3FJTC/GJs4Dj/8cD2g74UXXoisrdXaz8xvzqwiX/nKV+iyyy7Tg8t4iwlQA3N9sWDJH/3oR1E3paphQptZQliQH6gcdqMyceJE/a6cuQxAuH0dxHURIoSD+RJZvML69etL25YtW0YDBgww7ZtKpQS3AQimn5mZ+sYbb9TvFp9++mnq1KkTHXrooZG2t5YC0BATohYW1Mfin2RzCvBGoVDQ78rZXHzLLbfo/4No+9rPdREihIPdibOgMhZRzcxOCxYs0DM12LZ//etfelopY8mSJboJCil2wffzJ598Qhs3btQHPUsDmzlzpm4RAZXdwbBMDNanTGiwx+x/1u8fffSRPmls2LBBj3BHnIKavi7CxDWsIMEwadIk2rRpE9155516RkwRNp5Zv7MAYP4xCL6vg7gupjTczguwDmX1EhYtWqS7AdhdOcuDnjJlij6BsPgEllHAAviY6QkE288LFy7Ut2/dupV69+5N3/nOd/RaIsA/TOyx1Fse1sfs4shEHptcmH+XuWOuvvpqPWsGBNvXrF4FszJdcMEF+jzC3F/AP2vXrtX7tKGhQYitYSnnbD4599xzhf1Z7MJvfvObCFpa3X394osvVnxdhAgBAAAAQCTAHQMAAACASIAIAQAAAEAkQIQAAAAAIBIgQgAAAAAQCRAhAAAAAIgEiBAAAAAARAJECAAAAAAiASIEAAAAiIDm5ma69dZb9Sq6rGI0WxiOVYou8rvf/Y7OOOMMOu200+gXv/iFUBL93Xff1QuDff7zn6crr7xSLypmXGiOVS9lnz1nzhzLNnz88cd6pWT2GTz//ve/9e2sTLtKIEIAAIHAqt2ySYv9sYkNAGBPPp/XF4CbPn26vvDbxRdfTNddd52+thZb6PDxxx/Xhchjjz2ml0hn62kVxcsNN9ygi5B//OMf9NnPflYXHXzlXlZ1mlUzZaXWJ0+erC/RYEUmk9HX8lq3bl1p27PPPquv8+V3+QK3QIQAADzD7o6Md0+s9DtbSZP91dfXR9o+AJKyovgVV1xBPXv21Euijxw5kurq6qipqUkXEOeff76+fEW3bt3oq1/9qr6NwZa7YPudd955ejl1tr7W+++/T2vWrNFfZ/uxbeycHDp0qG5lmTt3rm1bvvCFL5T2YWvtsLWlmAWG5/vf/76+H9vOltpgq0Iz2E0HW3bjiSeeoNGjR+vWHbdAhAAAAmHw4MH6XRv7Y5MmAMAbK1eupO3bt1OfPn1oxYoVdPDBB5deGzRokL7aOGP58uXCa2z1cSZW2Hb2frYeFNtf9l4rmHgoum1efvll3brCRAzPqaeeSn/5y1/0P2at+e1vfytYdT744AOaNWsW/ehHP3L9myFCAACeYItZ/fWvfxX8xuxP5o4pWkyK7yn6vu+55x7db83+Z8/ZdnYXxcNW9mV3VKNGjaLjjz+evvjFL+qTnhdTLwBJYc++OA4WF8Iu/uwi365du9Lr7DFbKI7B/udfK77O3sP+is9l77ViwIABlEqldLHCLClMlBgZM2aMbr1h7fvKV75Cb775pvA6s4wyywwTRW4pr8kLAAAuOOSQQ/QJjfmc2eTWv3//0lLeVmzcuFH3TTMLyc6dO+nhhx+mV199VfdDswntk08+obvuuouGDRumfx77bDYZs+3F72B3ef/3f/+nm5zZyrQAVAu5XI4mTpyoW0CYe4bRtm1b/Vwpwh4zAcBg//OvFV9n72F/xedFSwb/XrbSbTGIlcWc8DDhwW4GWHDs7bffLsSRsDay4FgWu/Lpp5/qQbL8itvMneTHAgpLCADAE3fffTedeOKJJUFSdMEwd4wVzMf8y1/+kp588kl9qXXGqlWrdDHCJkJ291QoFHRfN4MF4jEB0rVrV928y/ZjwXUMZlFh7wWgGigUCroFhFkhmOWQ/c9gwnvp0qWl/ZiFYuDAgSWrBf8as6KsXr1a396xY0f9vLF6Lzu3WLwH+2OxKDxnnnmmfo6edNJJeswJD3PVsPNzxowZNH/+fP185LN1iu32CkQIAEA5bGI88sgj9bul4sTHJkWWGcDu3Lp06aJv27x5cyn9kMF82ywQjrl0vve97+nb2MT3zjvvRPZbAAiSSZMm6eOcWQqz2azg+mCCgIkL9vqDDz6ob2Mwi+HevXv1bBmWKcOEwZAhQ+jAAw8svZdtYxYQdq4w0cCCXp1gNwgss2bChAmm19hnsYBzdi4zS+Uf//jHQH4/3DEAAOXw/mmWDmjcVryL4u+sivsU3T08XnzOAMSVtWvX6pY+Zglk9UCKTJ06Vbc2XnDBBfT1r39dt5awTBgWF8VgYuBnP/sZ/fSnP9XdmIceeqj+uAgTEbfddpseT8VEA0vnPeigg1y1id0syGBxWyxglVlLunfvrrfn0UcfrbgPIEIAAJ4pigBmBlYBm1TZhMcEC7tTZBaT4t0Y80mzKH0Aks4BBxygB3RbMX78eP1PxmGHHUaPPPKI5fnJRIgb2Ln12muvSV9jcVlFWHwJE0c8LG3Y6TOcgAgBAHimeFf13nvv0YUXXliqdxAULHiOmZpZ4OqXv/xl3RrCBAiLE2EBcmeffXZg3wUAiA7EhAAAPHPuuefqBYvY3RELemN+Z2YyDgoWIzJz5kw9tbdTp076dzAf+FFHHUXXX399YN8DAIiWlGZ0wgIAAAAAhAAsIQAAAACIBIgQAAAAAEQCRAgAAAAAIgEiBAAAAACRABECAAAAgEiACAEAAABAJECEAAAAACASIEIAAAAAEAkQIQAAAACIBIgQAAAAAEQCRAgAAAAAKAr+P1xsXgS9UB14AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# label the test set\n",
    "hfc_kwargs = {\n",
    "    \"series\": labels_test,\n",
    "    \"future_covariates\": features,\n",
    "    \"retrain\": False,\n",
    "    \"last_points_only\": True,\n",
    "}\n",
    "preds = model.historical_forecasts(**hfc_kwargs)\n",
    "\n",
    "# and plot the predictions against actuals\n",
    "labels_test.plot()\n",
    "preds.plot(label=\"predicted\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e31d0158-6c51-48c8-a553-954e4102a1f9",
   "metadata": {},
   "source": [
    "From a first glance, this looks great.\n",
    "\n",
    "### Classification Metrics and Evaluation\n",
    "\n",
    "Let's evaluate the performance. We compute the backtest using Darts' built-in metrics for classification. You can find all classification metrics [here](https://unit8co.github.io/darts/generated_api/darts.metrics.html) in the \"Classification Metrics\" section.\n",
    "\n",
    "**Note**: The high performance metrics you'll see are expected since we artificially created labels using the same patterns as our features. This demonstrates that the classification models work correctly, but it's not representative of real-world performance where the relationship between features and labels is more complex and unknown.\n",
    "\n",
    "Check out the metrics' [documentation](https://unit8co.github.io/darts/generated_api/darts.metrics.metrics.html#darts.metrics.metrics.f1) for more information about how to extract label-specific metrics, customize aggregation methods, and more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "807afc5d-e742-4320-a054-f0949601ddb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99580713, 0.98668281, 0.99765473, 0.99202272])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.backtest(\n",
    "    historical_forecasts=preds,\n",
    "    metric=[\n",
    "        metrics.accuracy,\n",
    "        metrics.precision,\n",
    "        metrics.recall,\n",
    "        metrics.f1,\n",
    "    ],\n",
    "    **hfc_kwargs,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1ae481f-d13a-4389-84df-233f05c3a856",
   "metadata": {},
   "source": [
    "We can also compute the confusion matrix over all predictions. The columns represent the predicted- and rows the actual labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0f5ffa4a-bd18-4902-8e66-27f55b3f8474",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high std, trend inc</th>\n",
       "      <th>high std, trend dec</th>\n",
       "      <th>low std, trend inc</th>\n",
       "      <th>low std, trend dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>high std, trend inc</th>\n",
       "      <td>267.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high std, trend dec</th>\n",
       "      <td>0.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low std, trend inc</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low std, trend dec</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     high std, trend inc  high std, trend dec  \\\n",
       "high std, trend inc                267.0                  1.0   \n",
       "high std, trend dec                  0.0                176.0   \n",
       "low std, trend inc                   0.0                  0.0   \n",
       "low std, trend dec                   0.0                  0.0   \n",
       "\n",
       "                     low std, trend inc  low std, trend dec  \n",
       "high std, trend inc                 0.0                 0.0  \n",
       "high std, trend dec                 0.0                 1.0  \n",
       "low std, trend inc                 12.0                 0.0  \n",
       "low std, trend dec                  0.0                20.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_vals = list(cats.values())\n",
    "cm = model.backtest(\n",
    "    historical_forecasts=preds,\n",
    "    metric=metrics.confusion_matrix,\n",
    "    metric_kwargs={\"labels\": label_vals},\n",
    "    **hfc_kwargs,\n",
    ")\n",
    "pd.DataFrame(cm, index=cats, columns=cats)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71b158aa-fef3-4eca-9eb9-32e3304ed90b",
   "metadata": {},
   "source": [
    "### Probabilistic forecasting\n",
    "\n",
    "Our classification models support two ways to generate probabilistic forecasts:\n",
    "- Compute the predicted class probability for each step in the forecast with `predict_likelihood_parameters=True`.\n",
    "- Compute sampled class label forecasts with `num_samples>1`.\n",
    "\n",
    "Below you can see the predicted probability for each label `i` over time (\"pred_labels_p{i}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e8601053-0a69-4704-ab92-95f5e9398eff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds_proba = model.historical_forecasts(\n",
    "    predict_likelihood_parameters=True, **hfc_kwargs\n",
    ")\n",
    "labels_test[:24].plot(label=\"actual label\", lw=4)\n",
    "preds_proba[:24].plot(label=\"pred\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bbf00f3-f3ef-42ec-83cd-86569e1be6f3",
   "metadata": {},
   "source": [
    "Computing the metrics also works for probabilistic forecasts. It will compare the actual labels against the label with the highest predicted probability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e72ae956-101d-497e-b5dd-b6075ade6ec4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high std, trend inc</th>\n",
       "      <th>high std, trend dec</th>\n",
       "      <th>low std, trend inc</th>\n",
       "      <th>low std, trend dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>high std, trend inc</th>\n",
       "      <td>267.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high std, trend dec</th>\n",
       "      <td>0.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low std, trend inc</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low std, trend dec</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     high std, trend inc  high std, trend dec  \\\n",
       "high std, trend inc                267.0                  1.0   \n",
       "high std, trend dec                  0.0                176.0   \n",
       "low std, trend inc                   0.0                  0.0   \n",
       "low std, trend dec                   0.0                  0.0   \n",
       "\n",
       "                     low std, trend inc  low std, trend dec  \n",
       "high std, trend inc                 0.0                 0.0  \n",
       "high std, trend dec                 0.0                 1.0  \n",
       "low std, trend inc                 12.0                 0.0  \n",
       "low std, trend dec                  0.0                20.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = model.backtest(\n",
    "    historical_forecasts=preds_proba,\n",
    "    metric=metrics.confusion_matrix,\n",
    "    metric_kwargs={\"labels\": label_vals},\n",
    "    **hfc_kwargs,\n",
    ")\n",
    "pd.DataFrame(cm, index=cats, columns=cats)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef8f2849-9215-48a2-b061-25259b1e987d",
   "metadata": {},
   "source": [
    "## Classification Models for Forecasting Tasks\n",
    "\n",
    "While we've demonstrated using classification models for time series labeling (predicting labels for the same time steps as the input features), these models can also be used for traditional forecasting tasks. In forecasting mode, you would predict categorical values for future time steps based on historical data.\n",
    "\n",
    "The workflow for forecasting with classification models is identical to regression tasks described in the [SKLearn Model examples](https://unit8co.github.io/darts/examples/20-SKLearnModel-examples.html), with the key difference being that we perform classification instead of regression. You would:\n",
    "\n",
    "1. Use `lags` to specify historical target values as features\n",
    "2. Optionally include `lags_past_covariates` and `lags_future_covariates` for additional features\n",
    "3. Set `output_chunk_length` to determine how many future steps to predict at once\n",
    "4. Use `predict()` to generate forecasts for future time steps\n",
    "\n",
    "This makes Darts' classification models versatile tools for both labeling and forecasting categorical time series data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "466cdb1e-61d9-4e0f-b527-bf7b64ec4049",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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